SAP-C02 AWS Certified Solutions Architect - Professional Question and Answers

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The scenario describes a central event broker and multiple microservices running in separate AWS accounts that all need to consume the same events. The requirement is to distribute events from a central location to many microservices across accounts in a scalable and loosely coupled way, as part of modernizing to a microservices architecture.

Amazon EventBridge is a serverless event bus service designed for event-driven architectures. It supports centralized event buses, rich content-based filtering with rules, and cross-account event routing. With EventBridge, you can create an event bus in a central account and define rules that match specific event patterns (for example, by microservice or event type). Each rule can have one or more targets, including event buses in other AWS accounts. This supports the pattern of having a central event bus in one account and distributing relevant events to other accounts, where each microservice consumes events either directly from its own event bus or through additional rules and targets in its own account.

In this solution, you create a new EventBridge event bus in the central account and grant the appropriate permissions for cross-account access (option B). You then define EventBridge rules on the central event bus, filtered per microservice or per event category, and configure the rules to send events to the respective event buses or targets in the microservices’ accounts. EventBridge handles the fan-out and delivery of events across accounts in a managed, scalable way, which aligns with the modernization goal and reduces the operational overhead of managing custom routing or polling logic.

Option A uses an SNS topic with SQS queues in each account. This is a valid fan-out pattern and supports cross-account subscriptions, but it is more suited to traditional pub/sub messaging and does not provide the event routing, filtering, and observability features that EventBridge offers for modern event-driven microservices. In scenarios that explicitly mention an event broker and modernization, EventBridge is the recommended service.

Option C is incorrect because Kinesis Data Streams is designed for high-throughput streaming data and requires building and managing consumer applications. The description in the option is also technically inaccurate; Kinesis does not “invoke” microservices directly as event targets in the same way as EventBridge or SNS does. Instead, applications must read from the stream.

Option D uses a single central SQS queue that all microservices read from. SQS provides at-least-once delivery to competing consumers, which means multiple consumers reading from the same queue will typically share messages rather than each getting all messages. This does not satisfy the requirement for multiple microservices to each receive the same events independently. It also reduces decoupling and observability compared to an event bus model.

Therefore, creating an Amazon EventBridge event bus in the central account with rules to distribute events across accounts (option B) best meets the requirements for distributing events from a central broker to multiple microservices across accounts in a modernized architecture.

Aurora Auto Scaling enables your Aurora DB cluster to handle sudden increases in connectivity or workload. When the connectivity or workload decreases, Aurora AutoScaling removes unnecessary Aurora Replicas so that you don ' t pay for unused provisioned DB instances

https://aws.amazon.com/blogs/architecture/accelerating-your-migration-to-aws/ Build a business case with AWS Migration Evaluator The foundation for a successful migration starts with a defined business objective (for example, growth or new offerings). In order to enable the business drivers, the established business case must then be aligned to a technical capability (increased security and elasticity). AWS Migration Evaluator (formerly known as TSO Logic) can help you meet these objectives. To get started, you can choose to upload exports from third-party tools such as Configuration Management Database (CMDB) or install a collector agent to monitor. You will receive an assessment after data collection, which includes a projected cost estimate and savings of running your on-premises workloads in the AWS Cloud. This estimate will provide a summary of the projected costs to re-host on AWS based on usage patterns. It will show the breakdown of costs by infrastructure and software licenses. With this information, you can make the business case and plan next steps.

it covers all the requirements mentioned in the question, it will allow collecting the detailed metrics, including process information and it provides a way to query and analyze the data using Amazon Athena.

Create an S3 Bucket:

Navigate to Amazon S3 in the AWS Management Console and create a new S3 bucket to store the game files.Enable static website hosting on this bucket.

Upload Game Files:

Upload the 5 GB game release package to the S3 bucket. Ensure that the files are publicly accessible if required for download.

Configure Amazon Route 53:

Set up a new domain or subdomain in Amazon Route 53 and point it to the S3 bucket. This allows users to access the game files using a custom URL.

Use Amazon CloudFront:

Create a CloudFront distribution with the S3 bucket as the origin. CloudFront is a content delivery network (CDN) that caches content at edge locations worldwide, improving download performance and reducing latency for users regardless of their location.

Publish the Download URL:

Use the CloudFront distribution URL as the download link for users to access the game files. CloudFront will handle the efficient distribution and caching of the content.

This solution leverages the scalability of Amazon S3 and the performance benefits of CloudFront to provide an optimal download experience for users globally while minimizing costs.

References

Amazon CloudFront Documentation

Amazon S3 Static Website Hosting

RDS Proxyoptimizes and pools DB connections. By combining this withPrivateLink + NLB, other accounts can securely access the proxy endpoint without going over the internet or setting up peering for every new account.

RDS Proxy Docs

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/rds-proxy.html

https://repost.aws/knowledge-center/ecs-fargate-service-auto-scaling

They all mention using spot instances and EKS based on EC2. A spot instance is not appropriate for a production server and the company is developing new application designed for AWS Fargate, which means we must plan the future cost improvement including AWS Fargate. https://aws.amazon.com/savingsplans/compute-pricing/

The company should attach a policy to the S3-access group to deny all S3 actions unless MFA is present. The company should request temporary credentials from AWS Security Token Service (AWS STS). The company should attach the temporary credentials in a profile that Amazon S3 will reference when the user performs actions in Amazon S3. This solution will meet the requirements because AWS STS is a service that enables you to request temporary, limited-privilege credentials for IAM users or for users that you authenticate (federated users). You can use MFA with AWS STS to provide an extra layer of security when requesting temporary credentials1. You can use the sts get-session-token AWS CLI command to request temporary credentials that include an MFA token2. You can then use these credentials with the AWS CLI to access Amazon S3 resources. To do this, you need to attach a policy to the IAM group that denies all S3 actions unless MFA is present3.You also need to create a profile in the AWS CLI configuration file that references the temporary credentials.

The other options are not correct because:

Attaching a policy to the S3 bucket to prompt the IAM user for an MFA code when the IAM user performs actions on the S3 bucket would not work because policies attached to S3 buckets cannot enforce MFA authentication. Policies attached to S3 buckets are resource-based policies that define what actions can be performed on the bucket and by whom. They do not have any logic to prompt for an MFA code or verify it.

Updating the trust policy for the S3-access group to require principals to use MFA when principals assume the group would not work because trust policies are used for roles, not groups. Trust policies are policies that define which principals can assume a role. They do not apply to groups, which are collections of IAM users that share permissions.

Creating an Amazon Route 53 Resolver DNS Firewall domain list that contains the allowed domains and configuring a DNS Firewall rule group with rules to allow or block requests based on the domain list would not help with enforcing MFA authentication for Amazon S3 actions. Amazon Route 53 Resolver DNS Firewall is a feature that enables you to filter and regulate outbound DNS traffic for your VPC. You can create reusable collections of filtering rules in DNS Firewall rule groups and associate them with your VPCs. You can specify lists of domain names to allow or block, and you can customize the responses for the DNS queries that you block. This feature is useful for controlling access to sites and blocking DNS-level threats, but not for requiring MFA authentication.

All features – The default feature set that is available to AWS Organizations. It includes all the functionality of consolidated billing, plus advanced features that give you more control over accounts in your organization. For example, when all features are enabled the management account of the organization has full control over what member accounts can do. The management account can apply SCPs to restrict the services and actions that users (including the root user) and roles in an account can access.https://docs.aws.amazon.com/organizations/latest/userguide/orgs_getting-started_concepts.html#feature-set

Option D is correct because Amazon ECR enhanced scanning integrates with Amazon Inspector and provides continuous vulnerability scanning for container images. Enhanced scanning covers operating system package vulnerabilities and programming language package vulnerabilities, which basic scanning does not fully satisfy. Amazon Inspector can also help prioritize remediation by showing where vulnerable images are actively deployed, such as in Amazon ECS or Amazon EKS environments. EventBridge integration allows findings to be routed into an existing event-driven workflow without custom scanning infrastructure. Option A uses basic scanning, which is insufficient for the stated language package and continuous monitoring requirements. Option B creates unnecessary custom scanning and storage logic. Option C incorrectly combines basic and enhanced scanning with manual triggers, which does not meet the least-operational-overhead requirement.

https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/AWSHowTo.cloudwatch.html Elastic Beanstalk automatically uses Amazon CloudWatch to help you monitor your application and environment status. You can navigate to the Amazon CloudWatch console to see your dashboard and get an overview of all of your resources as well as your alarms. You can also choose to view more metrics or add custom metrics.

Reference architecture -https://docs.aws.amazon.com/vpc/latest/privatelink/privatelink-access-saas.html

Note from documentation that Interface Endpoint is at client side

This solution will allow the detection logic to be run as soon as the image is uploaded to the S3 bucket, before it is served to users via the CloudFront distribution. This way, the detection logic can quickly identify any corrupted images and prevent them from being served to users, minimizing latency between ingestion and serving.

AWS Systems Manager State Manager is the correct service because it maintains a desired configuration state across managed nodes. An association can define that specific software, such as antivirus or monitoring agents, must be installed and running with the required configuration. This directly satisfies the requirement to ensure all 10,000 EC2 instances consistently run the latest SaaS agent configuration with the least administrative overhead. A custom Lambda that logs in to every machine is fragile, hard to secure, and operationally expensive. AWS Config is useful for evaluating resource configuration, but it is not the most direct tool for recurring software installation and configuration enforcement inside EC2 instances. Session Manager and user data are also poor choices because user data normally runs at launch and does not continuously enforce state. (AWS Documentation)

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To ensure that game assets are fetched from the closest region and have a fallback option in case the assets become unavailable in the closest region, a solution architect should leverage Amazon CloudFront, a global content delivery network (CDN) service. By creating an Amazon CloudFront distribution and setting up origin groups, the architect can specify multiple origins (in this case, the Application Load Balancers in each region). The primary origin will serve content under normal circumstances, and if the content becomes unavailable, CloudFront will automatically switch to the secondary origin. This approach not only meets the requirement of regional proximity and redundancy but also optimizes latency and enhances the gaming experience by serving assets from the nearest geographical location to the end-user.

AWS Documentation on Amazon CloudFront and origin groups provides detailed instructions on setting up distributions with multiple origins for high availability and performance optimization. Additionally, AWS whitepapers and best practices on content delivery and global applications offer insights into effectively utilizing CloudFront and other AWS services to achieve low latency and high availability.

Creating a single transit gateway in eu-west-1 allows for centralized and cost-effective connectivity between the VPCs involved, without the complexity of creating multiple peering connections or transit gateways.

This approach also allows for selective attachment of only the required VPCs, meeting the strict security and access requirements.

Route table configuration ensures traffic flows properly through the transit gateway, providing connectivity for the application without exposing other VPC resources.

This solution is the most cost-effective and operationally efficient choice for secure inter-Region communication.

Create an S3 Bucket:

Log in to the AWS Management Console and navigate to Amazon S3.

Create a new S3 bucket that will serve as the destination for the application data.

Deploy AWS Storage Gateway:

Download and deploy the AWS Storage Gateway virtual machine (VM) on your on-premises environment. This VM can be deployed on VMware ESXi, Microsoft Hyper-V, or Linux KVM.

Configure the File Gateway:

Configure the deployed Storage Gateway as a file gateway. This will enable it to present Amazon S3 buckets as SMB file shares to your on-premises applications.

Create a New File Share:

Within the Storage Gateway configuration, create a new file share that is associated with the S3 bucket you created earlier. This file share will use the SMB protocol, allowing your on-premises applications to access the S3 bucket as if it were a local SMB file share.

Copy Data to the File Gateway:

Use your preferred method (such as robocopy, rsync, or similar tools) to copy data from the on-premises storage to the newly created file gateway endpoint. This data will be stored in the S3 bucket, maintaining accessibility through SMB.

Ensure Secure and Efficient Data Transfer:

AWS Storage Gateway ensures that all data in transit is encrypted using TLS, providing secure data transfer to AWS. It also provides local caching for frequently accessed data, improving access performance for on-premises applications.

This approach allows your existing on-premises applications to continue accessing data via SMB while leveraging the scalability and durability of Amazon S3.

References

AWS Storage Gateway Overview【67】.

AWS DataSync and Storage Gateway Hybrid Architecture【66】.

AWS S3 File Gateway Details【68】.

This option meets the RPO and RTO requirements for both the application and database tiers and uses tools like Amazon DLM and RDS automated backups to create and manage the backups. Additionally, it uses Global Accelerator to ensure low latency after failover by directing traffic to the closest healthy endpoint.

A. High performance computing (HPC) workload cluster should be in a single AZ.

C. Elastic Fabric Adapter (EFA) is a network device that you can attach to your Amazon EC2 instances to accelerate High Performance Computing (HPC)

F. Amazon FSx for Lustre - Use it for workloads where speed matters, such as machine learning, high performance computing (HPC), video processing, and financial modeling.

Cluster – packs instances close together inside an Availability Zone. This strategy enables workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication that is typical of HPC applications.

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html

https://aws.amazon.com/es/blogs/mt/implement-aws-resource-tagging-strategy-using-aws-tag-policies-and-service-control-policies-scps/

With AWS PrivateLink, the marketing team can create an endpoint service to share theirinternal application with other accounts securely using private IP addresses. They can grant permission to specific AWS accounts to connect to the service and create interface VPC endpoints in the other accounts to access the application by using private IP addresses. This option does not require any changes to the network of the other business units, and it does not require peering or NATing. This solution is both scalable and secure.

https://aws.amazon.com/blogs/networking-and-content-delivery/connecting-networks-with-overlapping-ip-ranges/

This option allows the solutions architect to use a DeletionPolicy attribute to specify how AWS CloudFormation handles the deletion of an S3 bucket when the stack is deleted1. By setting the value of Delete, the solutions architect can instruct CloudFormation to delete the bucket and all of itscontents1. This option does not require any major changes to the application’s architecture or any additional resources.

Deletion policies

The application is business-critical with an SLA requirement of 99.95% uptime and is currently limited by an on-premises data center. The architecture includes a PostgreSQL database and separate business logic and presentation layers. Remote users experience high latency when accessing the application, which is latency-sensitive.

To meet or exceed a 99.95% SLA for the database while minimizing operational burden, a managed database service with built-in high availability is preferred. Amazon Aurora PostgreSQL-compatible editions provide high availability and durability by automatically replicating data across multiple Availability Zones in a Region. Aurora can offer higher availability and faster failover compared to self-managed databases on EC2, and offloads patching, backups, and maintenance.

For the application and presentation layers, running them on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer preserves the basic architecture (stateless application servers behind a load balancer) while moving to a managed, highly available environment that can scale out and in automatically based on demand. This results in minimal changes to the application components and provides improved resilience compared to on-premises VMs.

Remote users experience slow load times because their desktop clients connect over higher-latency networks to the on-premises servers. By using Amazon AppStream 2.0, the application can be streamed from AWS to end users. AppStream 2.0 runs the desktop application close to the backend database and application servers within AWS, and streams only the display and input over the network. This significantly reduces the effect of latency between the user and the application backend, improving user experience, especially for latency-sensitive desktop applications, without requiring major changes to the application itself.

Option A uses a PostgreSQL database on EC2, which requires the customer to manage availability, backups, patching, and failover. This does not provide the same managed high-availability guarantees as Aurora or RDS Multi-AZ. It also suggests allocating a full Amazon WorkSpaces desktop per user, which can be more expensive and more complex than streaming only the application via AppStream 2.0.

Option C uses Amazon RDS for PostgreSQL with Multi-AZ configuration, which provides high availability and is a valid managed option. However, it runs the application and presentation layers on Fargate containers behind a Network Load Balancer. NLB is typically used for TCP-level load balancing and is better suited for protocols that require low-level handling, whereas HTTP/HTTPS web applications are commonly placed behind an Application Load Balancer, which provides advanced HTTP routing features. Also, ElastiCache can reduce database load and improve response times but does not directly solve the end-user network latency issue in a desktop client scenario.

Option D is incorrect because Amazon Redshift is a data warehouse service, not an operational PostgreSQL-compatible database for transaction processing. It is not appropriate for hosting the application’s primary relational database. CloudFront accelerates delivery of static and cached web content, not interactive desktop client-server traffic.

Therefore, migrating the database to Aurora PostgreSQL, running the application and presentation layers on EC2 Auto Scaling behind an Application Load Balancer, and using Amazon AppStream 2.0 to deliver the application to remote users (option B) meets the availability requirements, improves user experience, requires relatively little change to the application architecture, and minimizes operational costs compared to more complex or less-managed alternatives.

B is correct because the problem is caused by inconsistent query-string casing leading to CloudFront cache misses, and the best place to normalize the request is at the CloudFront edge before cache-key evaluation. CloudFront Functions run at the edge with very low latency and are intended for lightweight request manipulations (for example, normalizing URLs, headers, and query strings) on viewer request events. By sorting query parameters and converting them to lowercase on the viewer request, CloudFront will treat semantically equivalent requests as the same cache key, increasing cache hit ratio and reducing the number of origin calls to API Gateway and downstream Lambda invocations. This directly reduces latency for global users.

Why the other options are less suitable:

A (Lambda@Edge on origin request): Lambda@Edge can also modify requests, but it is heavier-weight than CloudFront Functions for simple normalization logic. Also, doing this on origin request occurs after CloudFront has already decided whether there is a cache hit. If the cache key is still based on the original mixed-case query string at the viewer side, you won’t consistently prevent cache misses. Normalization must happen before cache lookup to maximize cache hits.

C (API Gateway mapping templates): This normalizes the request only after it has already missed in CloudFront and reached the origin. It does not fix CloudFront cache fragmentation, so it does not meet the “minimal latency” goal as effectively.

D (Lambda authorizer): Lambda authorizers are for authorization/authentication decisions. Using an authorizer for query normalization adds unnecessary overhead and still happens at API Gateway (origin), not at CloudFront before cache evaluation.

Comprehensive and Detailed in Depth Explanation:

A is correct because AWS CodeBuild is a fully managed build service that supports running tests and security scans. EventBridge can detect test failures and send notifications via SNS. AWS CDK with manifest files is a robust way to manage feature toggles. CodePipeline supports manual approval stages to allow lead developer intervention.

The modernized application needs to store and serve video files up to 4 GB. Large binary content should not be stored directly in a database because it negatively affects database performance, scaling, and backup/restore times. The standard AWS pattern is to store large objects in Amazon S3 and keep only references (such as keys or URLs) in the database.

User profile data is simple, text-based, and currently lives in a single table. This is a good fit for a key-value or document-style data model such as Amazon DynamoDB, which provides fully managed horizontal scaling and predictable performance at scale.

Option B migrates the profile table to DynamoDB using AWS DMS with AWS Schema Conversion Tool (SCT). Video files are stored as objects in Amazon S3, which is designed for large, durable, and highly scalable object storage. The DynamoDB item stores only the S3 key that corresponds to each user’s video, keeping the database small and responsive. S3 can scale rapidly and independently of the database, and offloads the heavy I/O load of large objects away from the transactional store, so overall application performance is not negatively impacted by video storage or retrieval.

Option A stores the videos as base64-encoded strings in a TEXT column in a relational database. Storing multi-gigabyte objects directly in a relational database table leads to large row sizes, larger indexes, longer backup times, and degraded performance as the table grows. This does not meet the requirement to avoid affecting application performance.

Option C uses Amazon Keyspaces (for Apache Cassandra) as the profile store and S3 for video. While storing videos in S3 is correct, Keyspaces is typically chosen when there is already a Cassandra-based workload or a need for Cassandra-compatible interfaces. For a simple single-table user profile store being migrated from MySQL, DynamoDB is the more natural, managed choice and aligns better with AWS migration and modernization guidance.

Option D stores the videos as base64-encoded strings in DynamoDB items. DynamoDB has a maximum item size of 400 KB, which is far smaller than the 4 GB video size requirement. This makes option D technically infeasible.

Therefore, migrating user profile data to DynamoDB and storing video files in Amazon S3, with S3 keys stored in the corresponding DynamoDB items (option B), provides scalable video storage and protects application performance.

Migration Hub Discovery Agentcollectsdeep host-level data, includingprocesses, connections, dependencies, etc. Combined withStrategy Recommendations, it offers accurate insights for planning cloud migration.

Migration Hub Strategy Recommendations

The company wants a centralized enforcement mechanism across hundreds of AWS accounts. The control must ensure that any S3 access points that product teams create are VPC-only and cannot be internet-accessible. The most operationally efficient solution is one that applies across the organization without requiring per-account deployments, per-bucket policies, or per-access-point configuration.

Service control policies (SCPs) in AWS Organizations are designed to provide centralized guardrails that define the maximum available permissions for accounts in an organization. By attaching an SCP at the organization root (or to OUs as needed), the company can enforce that no principal in any account can create an S3 access point unless the request specifies a VPC network origin. This aligns directly with the requirement to enforce “VPC-only” access points consistently across all accounts with minimal ongoing operational work.

Option B uses an SCP at the root to deny s3:CreateAccessPoint unless the s3:AccessPointNetworkOrigin condition key evaluates to VPC. This is the correct organization-wide preventive control.

Option A is not correct because an S3 access point resource policy is attached to an access point after it exists and is primarily used to control access to the access point. It is not a reliable organization-wide preventive mechanism to enforce how access points are created across hundreds of accounts. Also, it requires access point-by-access point management, which increases operational overhead.

Option C is less operationally efficient because StackSets deployment across hundreds of accounts introduces additional operational steps and drift management. It also relies on account-level IAM permissions being properly used and does not provide a simple, centralized “cannot be bypassed” guardrail in the same way that an SCP does.

Option D is incorrect because S3 bucket policies control access to bucket resources and objects. They do not function as an organization-wide control to prevent creation of access points across accounts, and they would require bucket-by-bucket management rather than a centralized enforcement mechanism.

Therefore, a root-level SCP that denies access point creation unless the access point network origin is VPC is the most operationally efficient enforcement approach.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/create-asg-instance-type-requirements.html#use-attribute-based-instance-type-selection-prerequisites

Option C is correct because AWS Systems Manager Patch Manager is the AWS-native service for scalable patch management across managed instances. Patch baselines define approved patches, while delegated administration and OU targeting allow centralized control across many accounts in AWS Organizations. Applying patches first to the non- production OU and then to the production OU with approval gates satisfies the security policy and reduces rollout risk. Systems Manager Compliance and AWS CloudTrail provide auditability for patch status and administrative actions. Option A relies heavily on custom Automation and EventBridge logic, which increases overhead. Option B requires account-by-account configuration and does not scale cleanly across thousands of instances. Option D uses AWS Config for detection, but Config does not provide full patch orchestration by itself.

Using an Amazon Kinesis data stream as a target for the IoT Core rule ensures durable and scalable buffering of data during traffic spikes.

An ECS Fargate application reads data from Kinesis, processes it, and stores it in Aurora, meeting the requirement for flexible, serverless data handling.

This solution guarantees no data loss while providing real-time or near-real-time data processing capabilities.

The correct design is to use CloudWatch usage metrics and service quota metric math. AWS publishes usage metrics for some services in the AWS/Usage namespace, and these metrics correspond to service quotas. CloudWatch can use the SERVICE_QUOTA metric math function to compare current usage against the quota and trigger an alarm when usage approaches a defined threshold. This is exactly the requirement: notify the team when Fargate task usage reaches 80% of the maximum allowed tasks. Amazon SNS is the standard low-overhead notification target for CloudWatch alarms. Polling ECS with Lambda is unnecessary custom engineering. AWS Config evaluates resource compliance, not quota utilization trends. Monitoring each ECS service’s sample count would not accurately represent account-level Fargate task quota consumption. (AWS Documentation)

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The correct answer is D. Deploy the application on Amazon Elastic Kubernetes Service (Amazon EKS) with AWS Fargate and enable auto scaling behind an Application Load Balancer. Create additional read replicas for the DB instance. Create an Amazon Managed Streaming for Apache Kafka cluster and configure the application services to use the cluster. Store static content in Amazon S3 behind an Amazon CloudFront distribution.

Option D meets the requirements of the scenario because it allows you to reduce operational overhead and support the product release by using the following AWS services and features:

Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed service that allows you to run Kubernetes applications on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use Amazon EKS to deploy your containerized application services on a Kubernetes cluster that is compatible with your existing tools and processes.

AWS Fargate is a serverless compute engine that eliminates the need to provision and manage servers for your containers. You can use AWS Fargate as the launch type for your Amazon EKS pods, which are the smallest deployable units of computing in Kubernetes. You can also enable auto scaling for your pods, which allows you to automatically adjust the number of pods based on the demand or custom metrics.

An Application Load Balancer (ALB) is a load balancer that distributes traffic across multiple targets in multiple Availability Zones using HTTP or HTTPS protocols. You can use an ALB to balance the load across your Amazon EKS pods and provide high availability and fault tolerance for your application.

Amazon RDS for PostgreSQL is a fully managed relational database service that supports the PostgreSQL open source database engine. You can create additional read replicas for your DB instance, which are copies of your primary DB instance that can handle read-only queries and improve performance. You can also useread replicas to scale out beyond the capacity of a single DB instance for read-heavy workloads.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open source platform for building real-time data pipelines and streaming applications. You can use Amazon MSK to create and manage a Kafka cluster that is highly available, secure, and compatible with your existing Kafka applications. You can also configure your application services to use the Amazon MSK cluster as a source or destination of streaming data.

Amazon S3 is an object storage service that offers high durability, availability, and scalability. You can store static content such as images, videos, or documents in Amazon S3 buckets, which are containers for objects. You can also serve static content directly from Amazon S3 using public URLs or presigned URLs.

Amazon CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. You can use Amazon CloudFront to create a distribution that caches static content from your Amazon S3 bucket at edge locations closer to your users. This can improve the performance and user experience of your application.

Option A is incorrect because creating an EC2 Auto Scaling group behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances for your containers. Creating additional read replicas for the DB instance would not provide high availability or fault tolerance in case of a failure of the primary DB instance, unlike deploying the DB instance in Multi-AZ mode. Creating Amazon Kinesis data streams would not be compatible with your existing Apache Kafka applications, unlike using Amazon MSK.

Option B is incorrect because creating an EC2 Auto Scaling group behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances for your containers. Creating Amazon Kinesis data streams would not be compatible with your existing Apache Kafka applications, unlike using Amazon MSK. Storing and serving static content directly from Amazon S3 would not provide optimal performance and user experience, unlike using Amazon CloudFront.

Option C is incorrect because deploying the application on a Kubernetes cluster created on the EC2 instances behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances and Kubernetes control plane for your containers. Using Amazon API Gateway to interact with the application would add an unnecessary layer of complexity and cost to your architecture, as you would need to create and maintain an API gateway that proxies requests to your ALB.

The best solution is to provision a new Amazon Elastic File System (Amazon EFS) file system that uses Max I/O performance mode and mount the EFS file system on each EC2 instance in the cluster. Amazon EFS is a fully managed, scalable, and elastic file storage service that supports the POSIX standard and can be accessed by multiple EC2 instances concurrently. Amazon EFS offers two performance modes: General Purpose and Max I/O. Max I/O mode is designed for highly parallelized workloads that can tolerate higher latencies than the General Purpose mode. Max I/O mode provides higher levels of aggregate throughput and operations per second, which are suitable for big data analytics applications. This solution meets all the requirements of the company. References: Amazon EFS Documentation, Amazon EFS performance modes

A Direct Connect gateway allows you to connect multiple VPCs across different Regions to a Direct Connect connection1. A public VIF allows you to access AWS public services such as EC21. A Site-to-Site VPN connection over the public VIF provides encryption and redundancy for the traffic between the on-premises data center and the VPCs2. This solution is cheaper than setting up additional Direct Connect connections or using a private VIF with VPC peering.

The best solution is to deploy an AWS Lambda function to add capacity to the Amazon Redshift cluster by using an elastic resize operation when the cluster’s CPU metrics in Amazon CloudWatch reach 80%. This solution will enable the cluster to scale up or down quickly by adding or removing nodes within minutes. This will improve the performance of the complex read queries and also reduce the cost by scaling down when the demand decreases. This solution is more cost-effective than using a classic resize operation, which takes longer and requires more downtime. It is also more suitable than using Amazon EMR, which is designed for big data processing rather than data warehousing. References: Amazon Redshift Documentation, Resizing clusters in Amazon Redshift, [Amazon EMR Documentation]

B is correct because Amazon FSx for Lustre provides a high-performance, POSIX-compliant file system that can be directly linked with Amazon S3 using data repository integration. This allows the EC2-based processing engine (which requires POSIX file operations) to read/write files through a mounted Lustre file system while automatically importing objects from S3 and exporting results back to S3 using Lustre’s S3 integration policies. This satisfies all requirements: POSIX file access, no need to change the application to use the S3 API, and automated synchronization for both inputs and outputs.

Why the other options are incorrect:

A: DataSync is designed for scheduled or task-based data transfers. It does not provide a mounted POSIX file system interface for the application to read/write “in place,” and the “without an agent” requirement is not a fit for an EC2-hosted POSIX workflow that needs continuous file semantics.

C: Amazon EFS is POSIX and mountable, but “data repository associations to S3” is not an EFS capability in the way described. EFS does not provide native bidirectional automatic sync with S3 using import/export policies like FSx for Lustre’s S3 integration.

D: S3 File Gateway provides a file interface backed by S3, but keeping cache coherent with explicit RefreshCache calls is operationally complex and not truly “automatic synchronization” in the sense required. It also introduces caching behaviors that can complicate immediate availability expectations.

Option B is correct because AWS Organizations allows a company to create a new organization from a chosen payer account and define an organizational unit hierarchy. This way, the finance department can have a centralized method for payment but also maintain visibility into each group’s spending to allocate costs. The company can also invite the existing accounts to join the organization and create new accounts using Organizations, which simplifies the account management process.

Option D is correct because enabling all features of AWS Organizations and establishing appropriate service control policies (SCPs) that filter IAM permissions for sub-accounts allows the security team to have a centralized mechanism to control IAM usage in all the company’s accounts. SCPs are policies that specify the maximum permissions for an organization or organizational unit (OU), and they can be used to restrict access to certain services or actions across all accounts in an organization.

Option A is incorrect because using a collection of parameterized AWS CloudFormation templates defining common IAM permissions that are launched into each account requires more effort than using SCPs. Moreover, it does not provide a centralized mechanism to control IAM usage, as each account would have to launch the appropriate stacks to enforce the least privilege model.

Option C is incorrect because requiring each business unit to use its own AWS accounts does not provide a centralized method for payment or a centralized mechanism to control IAM usage. Tagging each AWS account appropriately and enabling Cost Explorer to administer chargebacks may help with cost allocation, but it is not as efficient as using AWS Organizations.

Option E is incorrect because consolidating all of the company’s AWS accounts into a single AWS account does not provide visibility into each group’s spending or a way to control IAM usage for different business units. Using tags for billing purposes and the IAM’s Access Advisor feature to enforce the least privilege model may help with cost optimization and security, but it is not as scalable or flexible as using AWS Organizations.

AWS Organizations

Service Control Policies

AWS CloudFormation

Cost Explorer

IAM Access Advisor

The CloudFront managed prefix list contains the IP ranges for all CloudFront edge locations. By updating the ALB security group ingress to allow access only from this prefix list, the web application will be accessible only by using the CloudFront endpoint. This solution requires the least amount of effort compared to the other options, which involve creating new resources or updating existing ones. This solution also avoids hard-coding IP addresses, which can change over time.

The best answer is C because the workload has millions of small 64 KB uploads every hour. Sending every S3 object event through EventBridge to an ECS task creates high event and invocation overhead. Amazon Data Firehose can buffer many incoming records and deliver larger compressed objects to S3, which reduces the small-object problem and improves cost efficiency. The ECS processing takes an average of 15 minutes, so replacing it with Lambda is risky because Lambda has a maximum runtime of 900 seconds, exactly 15 minutes. For five-year retention, S3 Lifecycle rules can transition data to S3 Glacier Deep Archive for long-term low-cost archival. DynamoDB TTL is appropriate for automatically expiring 15-day results without custom cleanup jobs. (AWS Documentation)

===============

https://aws.amazon.com/about-aws/whats-new/2017/11/announcing-support-for-inter-region-vpc-peering/

https://aws.amazon.com/es/blogs/industries/how-to-monitor-alert-and-remediate-non-compliant-hipaa-findings-on-aws/

The company should use Migration Evaluator to generate a list of servers and build a report for a business case. The company should use AWS Migration Hub to view the portfolio and use AWS Application Discovery Service to gain an understanding of application dependencies. This solution will meet the requirements because Migration Evaluator is a migration assessment service that helps create a data-driven business case for AWS cloud planning and migration. Migration Evaluator provides a clear baseline of what the company is running today and projects AWS costs based on measured on-premises provisioning and utilization1. Migration Evaluator can use an agentless collector to conduct broad-based discovery or securely upload exports from existinginventory tools2. Migration Evaluator integrates with AWS Migration Hub, which is a service that provides a single location to track the progress of applicationmigrations across multiple AWS and partner solutions3. Migration Hub also supports AWS Application Discovery Service, which is a service that helps systems integrators quickly and reliably plan application migration projects by automatically identifying applications running in on-premises data centers, their associated dependencies, and their performance profile4.

https://aws.amazon.com/migration-evaluator/

https://docs.aws.amazon.com/migration-evaluator/latest/userguide/what-is.html

https://aws.amazon.com/migration-hub/

https://aws.amazon.com/application-discovery/

https://aws.amazon.com/server-migration-service/

https://aws.amazon.com/dms/

https://docs.aws.amazon.com/controltower/latest/userguide/what-is-control-tower.html

https://aws.amazon.com/application-migration-service/

https://aws.amazon.com/storagegateway/

" The S3 buckets have millions of objects " If there are million of objects then you should use Batch operations. https://aws.amazon.com/blogs/storage/encrypting-objects-with-amazon-s3-batch-operations/

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/stacksets-orgs-manage-auto-deployment.html

migrating the data processing script to an AWS Lambda function and using an S3 event notification to invoke the Lambda function to process the objects when the company uploads the objects. This solution meets the company ' s requirements of high availability and scalability, as well as reducing long-term management overhead, and is likely to be the most cost-effective option.

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/resolver.html

The company should deploy a new Amazon Elastic File System (Amazon EFS) Multi-AZ file system. The company should configure the file system for 75 MiBps of provisioned throughput. The company should implement replication to a file system in the DR Region. This solution will meet the requirements because Amazon EFS is a serverless, fully elastic file storage service that lets you share file data without provisioning or managing storage capacity and performance. Amazon EFS is built to scale on demand to petabytes without disrupting applications, growing and shrinking automatically as you add and remove files1. By deploying a new Amazon EFS Multi-AZ file system, the company can create a single location for updates to application data for all instances. A Multi-AZ file system replicates data across multiple Availability Zones (AZs) within a Region, providing high availability and durability2. By configuring the file system for 75 MiBps of provisioned throughput, the company can ensure that it meets the peak operations requirement of 225 MiBps of read throughput. Provisioned throughput is a feature that enables you to specify a level of throughput that the file system can drive independent of the file system’s size or burst credit balance3. By implementing replication to a file system in the DR Region, the company can make a copy of the data available in another AWS Region for disaster recovery. Replication is a feature that enables you to replicate data from one EFS file system to another EFS file system across AWS Regions. The replication process has an RPO of less than 1 hour.

The other options are not correct because:

Deploying a new Amazon FSx for Lustre file system would not provide a single location for updates to application data for all instances. Amazon FSx for Lustre is a fully managed service that provides cost-effective, high-performance storage for compute workloads. However, it does not support concurrent write access from multiple instances. Using AWS Backup to back up the file system to the DR Region would not provide real-time replication of data. AWS Backup is a service that enables you to centralize and automate data protection across AWS services. However, it does not support continuous data replication or cross-Region disaster recovery.

Deploying a General Purpose SSD (gp3) Amazon Elastic Block Store (Amazon EBS) volume with 225 MiBps of throughput would not provide a single location for updates to application data for all instances. Amazon EBS is a service that provides persistent block storage volumes for use with Amazon EC2 instances. However, it does not support concurrent access from multiple instances, unless Multi-Attach is enabled. Enabling Multi-Attach for the EBS volume would not provide Multi-AZ resilience or cross-Region replication. Multi-Attach is a feature that enables you to attach an EBS volume to multiple EC2 instances within the same Availability Zone. Using AWS Elastic Disaster Recovery to replicate the EBS volume to the DR Region would not provide real-time replication of data. AWS Elastic Disaster Recovery (AWS DRS) is a service that enables you to orchestrate and automate disaster recovery workflows across AWS Regions. However, it does not support continuous data replication or sub-hour RPOs.

Deploying an Amazon FSx for OpenZFS file system in both the production Region and the DR Region would not be as simple or cost-effective as using Amazon EFS. Amazon FSx for OpenZFS is a fully managed service that provides high-performance storage with strong data consistency and advanced data management features for Linux workloads. However, it requires more configuration and management than Amazon EFS, which is serverless and fully elastic. Creating an AWS DataSync scheduled task to replicate the data from the production file system to the DR file system every 10 minutes would not provide real-time replication of data. AWS DataSync is a service that enables you to transfer data between on-premises storage and AWS services, or between AWS services. However, it does not support continuous data replication or sub-minute RPOs.

The correct answer is C. This option uses AWS CloudTrail to create a trail in the organization’s management account that applies to all accounts in the organization. This way, the company can centrally manage and audit all API calls to AWS resources across multiple accounts and regions. The company also needs to create a new Amazon S3 bucket with versioning turned on to store the logs. Versioning helps protect against accidental or malicious deletion of log files by keeping multiple versions of each object in the bucket. The company also needs to enable MFA delete and encryption on the S3 bucket to further enhance the security and durability of the data store.

Option A is incorrect because it uses an existing S3 bucket in the organization’s management account to store the logs. This may not be optimal for regulatory compliance, as the existing bucket may have different permissions, encryption settings, or lifecycle policies than a dedicated bucket for CloudTrail logs.

Option B is incorrect because it requires creating a new CloudTrail trail in each member account of the organization. This adds operational overhead and complexity, as the company would need to manage multiple trails and S3 buckets across multiple accounts and regions.

Option D is incorrect because it requires configuring Amazon SNS to send log-file delivery notifications to an external management system that will track the logs. This adds unnecessary complexity and cost, as CloudTrail already provides log-file integrity validation and log-file digest delivery features that can help verify the authenticity and integrity of log files.

Comprehensive and Detailed Explanation:

Why C is the best answer

This question requires all of the following:

Bidirectional access between two separate AWS Organizations

Access to S3 buckets across accounts and organizational boundaries

Encryption at rest

Logging of all S3 bucket access

Buckets exist in different Regions

Option C is the best match because S3 Access Points are purpose-built to simplify and scale access management for shared S3 datasets. For cross-account or multi-team sharing scenarios, S3 Access Points let you define dedicated access paths and policies instead of overloading a single bucket policy. For encryption at rest, customer managed AWS KMS keys are the strongest fit when cross-account access must be explicitly controlled. For logging bucket-level object access, AWS CloudTrail is the correct service because S3 data events record object-level API activity such as GetObject and PutObject.

Why the other options are not correct

A. S3 Access Grants is not the best fit here

S3 Access Grants is designed primarily to grant S3 access to identities mapped from identity sources and simplify data access at scale. It is not the standard primary design choice for bidirectional bucket sharing between two separate organizations. The exam-focused architectural pattern for controlled cross-account S3 sharing is S3 Access Points plus policies, combined with KMS permissions and CloudTrail data events. Even though SSE-KMS and CloudTrail data events are good parts of the design, the access mechanism in A is not the best answer.

B. VPC networking services do not solve the main requirement

VPC peering, PrivateLink, and endpoint access controls are network connectivity controls. Amazon S3 is not shared between organizations by building VPC peering between their VPCs. Also, VPC Flow Logs record network flow metadata, not S3 object access. That means B fails the logging requirement and uses the wrong control plane for the access-sharing requirement.

D. Replication and SCPs do not provide bidirectional shared access

S3 Cross-Region Replication copies objects; it does not establish bidirectional shared access for both organizations to each other’s buckets. Service Control Policies also do not work the way this option describes. SCPs are guardrails within an AWS Organization; they are not shared between separate organizations to grant object access. In addition, S3 server access logging is not the best answer when the requirement is to collect logs of all S3 bucket access in an auditable way for object-level API actions. CloudTrail data events are the stronger and more exam-aligned choice for logging object access activity.

Key technical reasoning

Cross-account / cross-organization S3 access:

S3 Access Points are commonly used to provide distinct permissions and simplified policy management for different consumers of the same bucket. This is especially useful in multi-account environments.

Encryption at rest:

When access spans accounts or organizations, customer managed KMS keys are preferred because key policies and grants can explicitly authorize external principals. This gives finer control than SSE-S3 when cross-account governance matters.

Logging:

For S3 object-level operations, CloudTrail data events are the standard answer in AWS architecture questions. They record API activity such as GetObject, PutObject, and DeleteObject. This is more appropriate than VPC Flow Logs or relying only on S3 server access logging.

Regional aspect:

The fact that buckets are in us-east-1 and us-west-1 does not prevent cross-account access. S3 access control, KMS permissions, and CloudTrail can be configured in the relevant Regions.

https://aws.amazon.com/premiumsupport/knowledge-center/cloudfront-https-connection-fails/

Using an Amazon S3 bucket

Using a MediaStore container or a MediaPackage channel

Using an Application Load Balancer

Using a Lambda function URL

Using Amazon EC2 (or another custom origin)

Using CloudFront origin groups

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/restrict-access-to-load-balancer.html

AWS IoT Core’spre-provisioning hookallows custom logic (like serial number validation)beforea device is registered. This ensures that only installed devices connect. Lambda is ideal for such logic.

AWS IoT Just-in-Time Provisioning

(https://docs.aws.amazon.com/controltower/latest/userguide/strongly-recommended-guardrails.html)

https://docs.aws.amazon.com/appsync/latest/devguide/graphql-overview.html

AWS AppSync is a fully managed GraphQL service that allows applications to securely access, manipulate, and receive data as well as real-time updates from multiple data sources1. AWS AppSync supports GraphQL subscriptions to perform real-time operations and can push data to clients that choose to listen to specific events from the backend1. AWS AppSync uses WebSockets to establish and maintain a secure connection between the clients and the API endpoint2. Therefore, using AWS AppSync and leveraging WebSockets is a suitable design to reduce comment latency and improve user experience.

The requirement is to continuously scan all AWS resources in this solution for security vulnerabilities with the least operational overhead. The environment includes Amazon EKS worker nodes in a managed node group and an Amazon ECR repository that stores container images used by the EKS cluster.

Amazon Inspector is a managed vulnerability scanning service that integrates directly with multiple AWS resource types. It can automatically discover Amazon EC2 instances (including EKS managed node group instances) and perform continuous vulnerability assessments against them, using software inventory and known CVE databases. Inspector also integrates with Amazon ECR to scan container images for vulnerabilities when images are pushed to the repository and periodically thereafter, providing continuous assessment of image security posture without requiring separate scanners or infrastructure.

By activating Amazon Inspector for the account and region, the service will automatically start tracking supported resources, including EKS-managed EC2 instances and ECR repositories, and will begin scanning them for known vulnerabilities. Findings are provided through the Inspector console and can be integrated with other services such as AWS Security Hub or Amazon EventBridge for further aggregation and automation, but no separate infrastructure deployment or maintenance is required.

Option B, therefore, provides end-to-end vulnerability scanning for both the EKS nodes and the ECR images with minimal operational overhead, because it leverages a fully managed AWS service that automatically discovers and scans supported resources.

Option A is not correct because AWS Security Hub does not itself perform vulnerability scanning. Security Hub aggregates and normalizes security findings from various AWS services, including Amazon Inspector, GuardDuty, and others. It is a security posture management and aggregation service, not a vulnerability scanner.

Option C introduces additional operational overhead by requiring the company to provision, configure, secure, and maintain a separate EC2 instance running a third-party vulnerability scanning tool. While ECR basic scan-on-push can detect some image vulnerabilities, the EC2-based scanner must be managed, updated, and scaled by the customer, which violates the requirement for the least operational overhead when a fully managed alternative exists.

Option D is incorrect because the Amazon CloudWatch agent is used for metrics and log collection from EC2 instances and other environments; it does not perform vulnerability scanning or CVE analysis. Configuring CloudWatch for monitoring and logging does not meet the requirement to continuously scan for security vulnerabilities. ECR basic scans also provide only image-level scanning and do not cover the EKS nodes themselves.

Therefore, enabling Amazon Inspector to scan both the EKS nodes and the ECR repository, as described in option B, is the solution that meets the continuous vulnerability scanning requirement with the least operational overhead.

The Auto Scaling group can automatically launch and terminate EC2 instances based on the demand and health of the web application. The launch template can specify the configuration of the EC2 instances, such as the AMI, instance type, security group, and user data. The existing ALB can distribute the traffic to the EC2 instances in the Auto Scaling group. The existing EC2 instances can be attached to the Auto Scaling group without deleting them or the ALB. This option minimizes downtime and preserves the current setup of the web application. References: [What is Amazon EC2 Auto Scaling?] , [Launch templates], [Attach a load balancer to your Auto Scaling group] , [Attach EC2 instances to your Auto Scaling group]

Because MSK has Maximum number of client connections 1000 per second and the company has 10,000 sensors, the MSK likely will not be able to handle all connectionshttps://docs.aws.amazon.com/msk/latest/developerguide/limits.html

The company needs centralized traffic inspection and centralized internet access for multiple workload VPCs connected by a transit gateway. The standard AWS hub-and-spoke pattern for centralized inspection is to use an inspection VPC that hosts network virtual appliances behind a Gateway Load Balancer, and to steer traffic from spoke VPCs through the transit gateway to the inspection VPC. Gateway Load Balancer endpoints are used to privately connect VPCs to the GWLB, allowing traffic to be transparently redirected to the appliances for inspection without changing application configurations.

To ensure symmetric routing for stateful inspection (so that return traffic traverses the same appliance path), appliance mode is enabled on the transit gateway attachment that connects to the inspection VPC. Appliance mode is specifically used with transit gateways and third-party appliances to preserve flow symmetry and avoid asymmetric routing issues.

Option D matches the centralized model: it creates a dedicated inspection VPC that contains the GWLB, endpoints, and the inspection appliance. It updates workload VPC routes to send traffic to the transit gateway and configures the transit gateway route tables to forward relevant traffic to the GWLB endpoints in the inspection VPC. Enabling appliance mode on the transit gateway is the key operational setting to maintain symmetric routing through the appliance fleet for inspected traffic.

Option A is incorrect because it places the inspection components inside an existing workload VPC rather than using a centralized inspection VPC. This increases coupling, makes the architecture harder to manage, and does not match the typical centralized inspection pattern. It also references enabling appliance mode on the GWLB, whereas appliance mode is a transit gateway attachment feature used for routing symmetry with appliances, not a setting “on the GWLB” itself.

Option B is incorrect because it splits the GWLB and endpoints/appliances across different workload VPCs, which complicates the design and is not the intended GWLB pattern. GWLB endpoints are created in VPCs that need to send traffic to the GWLB service; the appliances sit behind the GWLB in the provider/inspection VPC. The option also refers to enabling appliance mode on endpoints, but the appliance mode setting is associated with the transit gateway attachment.

Option C is incorrect because VPC flow logs are for logging and visibility; flow logs do not route traffic. Also, separating “inspection VPC” and “internet access VPC” with the appliances in the internet VPC does not align with the standard GWLB centralized inspection architecture and introduces unnecessary complexity.

Therefore, creating a dedicated inspection VPC with GWLB and endpoints and enabling appliance mode on the transit gateway attachment is the correct centralized approach.

Option A, Deploy the security tool on EC2 instances in a new Auto Scaling group in the existing VPC, allows the company to use its existing security tool while still running it within the AWS environment. This ensures that all packets coming in and out of the VPC are inspected by the security tool in real time. Option D, Provision a Gateway Load Balancer for each Availability Zone to redirect the traffic to the security tool, allows for high availability within an AWS Region. By provisioning a Gateway Load Balancer for each Availability Zone, the traffic is redirected to the security tool in the event of any failures or outages. This ensures that the security tool is always available to inspect the traffic, even in the event of a failure.

   

AWS Application Migration Service:

AWS Application Migration Service (MGN) facilitates the migration of virtual machines (VMs) to AWS without installing additional software on the VMs. This agentless service helps in seamlessly migrating workloads to AWS.

AWS Migration Hub Strategy Recommendations:

AWS Migration Hub Strategy Recommendations offer insights and guidance for planning and implementing migration strategies. It helps in generating a Total Cost of Ownership (TCO) report by automatically importing discovery data from the VMs.

Generating the TCO Report:

The combined use of AWS Application Migration Service and Migration Hub Strategy Recommendations enables the automatic import of discovery data and the generation of an accurate TCO report, ensuring a smooth and cost-effective migration process.

References

AWS Migration Hub Strategy Recommendations​(AWS Documentation)​.

https://docs.aws.amazon.com/application-discovery/latest/userguide/discovery-agent.htmlhttps://docs.aws.amazon.com/migrationhub/latest/ug/ec2-recommendations.html

https://aws.amazon.com/global-accelerator/faqs/

S3 Event Notifications is a feature that allows users to receive notifications when certain events happen in an S3 bucket, such as object creation or deletion1. Users can configure S3 Event Notifications to invoke an AWS Lambda function when a user stores an image in the bucket. Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources2. The Lambda function can resize the image in place and store the original file in the same S3 bucket. This way, the solution can handle significant variance in demand and be reliable at enterprise scale. Thesolution can also rerun processing jobs in the event of failure by using the retry and dead-letter queue features of Lambda2.

S3 Lifecycle is a feature that allows users to manage their objects so that they are stored cost-effectively throughout their lifecycle3. Users can create an S3 Lifecycle policy to move all stored images to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is a storage class designed for data that is accessed less frequently, but requires rapid access when needed4. It offers a lower storage cost than S3 Standard, but charges a retrieval fee. Therefore, moving the images to S3 Standard-IA after 6 months can reduce the storage cost for the solution.

Option A is incorrect because using AWS Step Functions to process the S3 event that occurs when a user stores an image is not necessary or cost-effective. AWS Step Functions is a service that lets users coordinate multiple AWS services into serverless workflows. However, for this use case, a single Lambda function can handle the image resizing task without needing Step Functions.

Option B is incorrect because using Amazon EventBridge to process the S3 event that occurs when a user uploads an image is not necessary or cost-effective. Amazon EventBridge is a serverless event bus service that makes it easy to connect applications with data from a variety of sources. However, for this use case, S3 Event Notifications can directly invoke the Lambda function without needing EventBridge.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) to process the S3 event that occurs when a user stores an image is not necessary or cost-effective. Amazon SQS is a fully managed message queuing service that enables users to decouple and scale microservices, distributed systems, and serverless applications. However, for this use case, S3 Event Notifications can directly invoke the Lambda function without needing SQS. Moreover, storing the resized file in an S3 bucket that uses S3 Standard-IA will incur a retrieval fee every time the file is accessed, which may not be cost-effective for frequently accessed files.

Gateway VPC Endpoint for S3:

Create a gateway VPC endpoint for Amazon S3 in your VPC. This allows instances in your VPC to communicate with Amazon S3 without going through the internet, reducing data transfer costs and improving security.

Add Spot Instances to ECS Cluster:

Add another ECS capacity provider that uses an Auto Scaling group of Spot Instances. Configure this new capacity provider to share the load with the existing On-Demand Instances by setting an appropriate weight in the capacity provider strategy. Spot Instances offer significant cost savings compared to On-Demand Instances.

Configure Capacity Provider Strategy:

Adjust the ECS service ' s capacity provider strategy to utilize both On-Demand and Spot Instances effectively. This ensures a balanced distribution of tasks across both instance types, optimizing cost while maintaining availability.

By implementing a gateway VPC endpoint for S3 and incorporating Spot Instances into the ECS cluster, the company can significantly reduce operational costs without compromising on the availability or performance of the platform.

References

AWS Cost Optimization Blog on VPC Endpoints

AWS ECS Documentation on Capacity Providers

The company does not have a data inventory and needs to identify which S3 buckets contain sensitive data. The appropriate AWS managed service for discovering and classifying sensitive data in S3 is Amazon Macie. Macie is designed to discover, classify, and report on sensitive data such as PII in S3 buckets. Amazon Inspector is primarily focused on vulnerability management for compute and container resources and does not provide S3 sensitive data classification in the way Macie does.

After identifying sensitive data locations, the company needs to ensure sensitive data is encrypted with a key that only administrators can access. SSE-S3 uses S3-managed keys and does not provide fine-grained administrative control of key usage in the same way as SSE-KMS with a customer managed key. Using AWS KMS customer managed keys allows the company to control access through key policies and IAM policies so that only designated administrator principals can use or manage the key.

The requirement also implies existing objects already encrypted with SSE-S3 need to be re-encrypted with SSE-KMS for sensitive objects. Changing default encryption only affects new objects. Existing objects must be rewritten (copied over themselves or copied to a new location) using SSE-KMS with the customer managed key. An orchestrated workflow is a common approach to iterate over identified objects and perform copy operations with the desired encryption settings.

Option C uses Macie for discovery, creates a KMS customer managed key restricted to administrators, sets bucket default encryption to SSE-KMS for future objects, and uses a Step Functions workflow to re-encrypt existing sensitive objects. This meets both the discovery requirement and the encryption/control requirement.

Option A is incorrect because Inspector is not the right service to inventory sensitive data in S3. Although the use of a customer managed KMS key and bucket policy enforcement is directionally correct for controlling encryption on writes, the first step (sensitive data discovery) is wrong.

Option B is incorrect because AWS managed keys cannot have their key policies modified by customers in the way customer managed keys can. Also, Inspector is not the right tool for sensitive data discovery in S3.

Option D is incorrect for the same reasons: it relies on Macie correctly for discovery but then attempts to modify an AWS managed key policy, which is not the correct method for restricting access. To restrict access, the company should use a KMS customer managed key with an appropriate key policy.

Therefore, using Amazon Macie plus an AWS KMS customer managed key and a workflow to re-encrypt existing sensitive objects is the correct solution.

The requirement has two separate controls: customer-controlled cryptographic root of trust and preventive regional governance. AWS KMS external key store (XKS) is the correct encryption model because AWS KMS forwards cryptographic operations to an external key manager that the customer controls outside AWS, such as an in-country HSM environment. That satisfies the requirement to operate the HSMs and retain control of the key root. AWS Control Tower Region deny control is the correct preventive governance control because it denies access to unlisted operations outside approved governed Regions for the selected organizational scope. AWS Config is detective, not preventive. AWS KMS multi-Region keys and standard customer managed keys do not keep the cryptographic root in customer-operated in-country HSMs. AWS CloudHSM custom key stores still use AWS CloudHSM clusters, not external in-country HSMs.

Kinesis Data Streams is a real-time streaming service and provide near-real-time analytics. Also the question " Deliver results of processing to a data warehouse " and this option has redshift cluster which is a powerful data warehousing solution that can handle large-scale analytics workloads.

AWS DataSync can be used to transfer the sequencing data to Amazon S3, which is a more efficient and faster method than using Snowball Edge devices. Once the data is in S3, S3 events can trigger an AWS Lambda function that starts an AWS Step Functions workflow. The Docker images can be stored in Amazon Elastic Container Registry (Amazon ECR) and AWS Batch can be used to run the container and process the sequencing data.

Create a Transit Gateway:

Step 1: In the AWS Management Console, navigate to the VPC Dashboard.

Step 2: Select " Transit Gateways " and click on " Create Transit Gateway " .

Step 3: Configure the transit gateway by providing a name and setting the options for Amazon side ASN and VPN ECMP support as needed.

Step 4: Attach each of the company’s VPCs and relevant subnets to the transit gateway. This centralizes the network management and simplifies the routing configurations, supporting scalable and flexible network architecture.

Set Up AWS PrivateLink:

Step 1: Create a Network Load Balancer (NLB) in the management VPC that points to the compute resource responsible for license validation.

Step 2: Create an AWS PrivateLink endpoint service pointing to this NLB.

Step 3: Allow each customer ' s VPC to create an interface endpoint to this PrivateLink service. This setup enables secure and private communication between the customer VPCs and the management VPC, ensuring one-way access from each customer ' s VPC to the management VPC for license validation.

This combination leverages the benefits of AWS Transit Gateway for scalable and centralized routing, and AWS PrivateLink for secure and private service access, meeting the requirement with minimal operational overhead.

References

Amazon VPC-to-Amazon VPC Connectivity Options

AWS PrivateLink - Building a Scalable and Secure Multi-VPC AWS Network Infrastructure

Connecting Your VPC to Other VPCs and Networks Using a Transit Gateway

An SCP at a lower level can ' t add a permission after it is blocked by an SCP at a higher level. SCPs can only filter; they never add permissions. SO you need to create a new OU for the new account assign an SCP, and move the root SCP to Production OU. Then move the new account to production OU when AWS config is done.

The AWS Application Discovery Service can help plan migration projects by collecting data about on-premises servers, such as configuration, performance, and network connections. The data collection agent is a lightweight software that can be installed on each server to gather this information. This option is more cost-effective than agentless discovery, which requires deploying a virtual appliance in the VMware environment, or using CloudWatch agent, which incurs additional charges for CloudWatch Logs. Scanning the servers over a VPN is not a valid option for AWS Application Discovery Service. References: What is AWS Application Discovery Service?, Data collection methods

This solution requires the least amount of effort as it only requires to update the API endpoint to private in API Gateway and create an interface VPC endpoint. Then create a resource policy and attach it to the API. This will make the API only accessible from the VPC and still keep the authentication mechanism intact. Reference:

https://aws.amazon.com/premiumsupport/knowledge-center/private-api-gateway-vpc-endpoint/

https://aws.amazon.com/api-gateway/features/

D is correct because thehealth check grace perioddefines how long Auto Scaling waits after launching an instance before checking its health status. With the larger content added to the S3 bucket, the instance initialization (via user data) is taking longer. Increasing the grace period allows the instance time to complete startup tasks before it’s marked as unhealthy.

Option A would not necessarily reduce startup time — the issue is likely network latency or the size of the content.

Option B only affects the timeout for health check responses, not the delay before they begin.

Option C is not applicable unless the health check path itself is invalid (which isn ' t mentioned).

Option D offers the lowest latency and highest efficiency:

Amazon DynamoDB global tables replicate data across multiple Regions, ensuring that the database is available and responsive in each Region.

Lambda@Edge allows the backend logic to execute at AWS edge locations, reducing the distance between the user ' s device and the code execution environment, thereby minimizing latency.

Amazon CloudFront serves as the content delivery network, routing requests to the nearest edge location where the Lambda@Edge function is executed.

Amazon Route 53 maps the DNS name to the CloudFront distribution, ensuring that users are directed to the optimal edge location.

This architecture ensures rapid response times and high availability for a globally distributed user base.

https://docs.aws.amazon.com/global-accelerator/latest/dg/about-accelerators.alb-accelerator.html Option A is wrong. AWS WAF does not support associating with NLB. https://docs.aws.amazon.com/waf/latest/developerguide/waf-chapter.html Option B is wrong. An ALB does not support an Elastic IP address.https://aws.amazon.com/elasticloadbalancing/features/

The correct answers are A and C. Cross-Region Replication requires an S3 replication rule from the source bucket to the destination bucket, and S3 Versioning must be enabled, which the question already states. Because the business requires availability in the second Region within 15 minutes, ordinary replication is not sufficient by itself. S3 Replication Time Control is designed for this type of compliance requirement and provides replication metrics and event notifications. AWS documentation specifically identifies OperationMissedThreshold as the event that notifies the bucket owner when an object eligible for S3 RTC replication exceeds the 15-minute threshold. DataSync is not the best fit for continuous object replication with a 15-minute compliance notification requirement. Transfer Acceleration improves upload performance to S3, not replication SLA tracking. (AWS Documentation)

===============

The company has many S3 buckets and many identities across multiple AWS accounts. It currently uses S3 bucket policies for granular authorization. As requirements grow, the bucket policies have become very large and have hit the maximum size limits. The company needs a way to express granular, identity-based access to S3 resources without relying on increasingly complex bucket policies.

AWS provides S3 Access Grants to centrally manage S3 data access based on identities. S3 Access Grants integrate with IAM Identity Center and external identity providers such as Microsoft Entra ID (formerly Azure AD). With S3 Access Grants, you define grants that associate specific identities or groups (from IAM Identity Center) with permissions on specific S3 buckets, prefixes, or objects. Access Grants maintain a mapping between identities and S3 resources without requiring large, per-bucket policies.

Option A describes exactly this approach. You create an S3 Access Grant configuration associated with the IAM Identity Center instance that is already integrated with the company’s IdP. You then define S3 Access Grants for the various user groups according to business requirements, specifying the appropriate S3 buckets and prefixes. When users access S3 through AWS tools that support S3 Access Grants, temporary credentials are issued that reflect the grants. This centralizes authorization management, reduces the size and complexity of S3 bucket policies, and minimizes operational overhead because you manage permissions per identity group rather than constantly editing long bucket policies.

Option B uses S3 access points and Object Lambda Access Points with a Lambda function for data filtering. This is more suitable for content transformation or redaction rather than primary authorization management. It adds operational complexity by requiring custom Lambda code and Object Lambda configuration, and it does not directly solve the problem of large bucket policies for fine-grained permissions.

Option C uses per-bucket S3 access points with attached policies. While access points can simplify some aspects of access control and allow per-application policies, using many access points with detailed policies can still lead to significant policy complexity. It also does not take advantage of the existing IAM Identity Center integration and does not centrally align permissions with identity groups.

Option D uses AWS Organizations service control policies (SCPs). SCPs are intended to define high-level guardrails and maximum permissions at the account or organizational unit level, not detailed, per-bucket or per-prefix access control for individual users or groups. Using SCPs to express granular S3 bucket permissions would be complex, hard to maintain, and not aligned with the purpose of SCPs.

Therefore, using S3 Access Grants integrated with IAM Identity Center (option A) provides a centralized, identity-aware, low-overhead way to manage granular S3 access without hitting S3 bucket policy size limits.

B is correct because the requirement is to allocate shared EC2 infrastructure costs (ECS on EC2, shared cluster) down to the business groups with the least operational effort. With shared compute, the EC2 line-item charges are not naturally per “task,” so you need a cost allocation feature that can split shared costs using workload metadata. Split cost allocation data combined with the AWS Cost and Usage Report (CUR) and resource tags provides the standard AWS mechanism to distribute (“split”) shared resource costs across consumers for chargeback/showback reporting. This avoids operationally heavy changes (like redesigning clusters) and supports automated reporting.

Why the other options are incorrect:

A: Tagging the Auto Scaling group can only attribute costs at the ASG level, not split costs between multiple business groups sharing the same cluster capacity. It does not solve per-business-group allocation when capacity is shared.

C: Separate clusters per group would work but is higher operational overhead (more clusters to manage, scaling policies, capacity planning, governance), which violates the “least operational overhead” requirement.

D: Cost Categories are useful for mapping and organizing costs, but by themselves they do not solve the underlying need to split shared EC2 costs among multiple consumers unless you already have an accurate split basis. Option B directly targets split allocation of shared costs with tagging and CUR.

For a development environment that requires frequent resource recreation and connectivity to applications hosted in a shared services account, the most efficient solution involves using AWS Resource Access Manager (RAM) and the transit gateway in the shared services account. By turning on automatic acceptance for the transit gateway in the shared services account and sharing it with the development account through AWS RAM, the development team can easily recreate their connection as needed without manual intervention. This setup allows for scalable, flexible connectivity between accounts while minimizing operational overhead and ensuring consistent access to shared services.

AWS Documentation on AWS Resource Access Manager and Transit Gateway provides guidance on sharing network resources across AWS accounts and enabling automatic acceptance for transit gateway attachments. This approach is also supported by AWS best practices for multi-account strategies using AWS Organizations and network architecture.

AWS Cost Anomaly Detection is a feature of the AWS Cost Management suite that leverages machine learning to enable continuous monitoring of your AWS costs and usage, allowing you to identify unexpected and abnormal spending1. You can create cost monitors that evaluate specific AWS services, member accounts, cost allocation tags, orcost categories based on your AWS account structure2. You can also configure alert subscriptions that notify you when a cost monitor detects an anomaly that meets your threshold2. In this case, you can create a cost monitor with a monitor type of AWS Service and apply a filter of Amazon EC2 to track the EC2 usage as a metric. You can then configure an alert subscription to notify the architecture team if the usage is 10% more than the average usage for the last 30 days, which is the anomaly detection period used by AWS Cost Anomaly Detection3.

to connect out from the private subnet you need an NAT gateway and since only one Elastic IP whitelisted on firewall its one NATGateway at time and if AZ failure happens Lambda creates a new NATGATEWAY in a different AZ using the Same Elastic IP ,dont be tempted to select D since application that needs to connect is on a private subnet whose outbound connections use the NATGateway Elastic IP

B:https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html#HowItWorks.requests

D:https://aws.amazon.com/blogs/compute/robust-serverless-application-design-with-aws-lambda-dlq/c

Comprehensive and Detailed in Depth Explanation:

B is correct because RDS Proxy allows for connection pooling and improved management of DB connections. Lambda + RDS often runs into connection exhaustion during high concurrency unless RDS Proxy is used. Provisioned concurrency prevents cold starts.

A is correct because the agent performs autonomous workload actions without user interaction. In OAuth terms, this maps to the client credentials grant, where the workload authenticates as itself rather than on behalf of an interactive user. Amazon Bedrock AgentCore Identity is designed for identity and credential management for AI agents and automated workloads, including workload identities, credential providers, and auditability. AWS documentation describes AgentCore Identity as supporting agent identities through workload identities and states that client credentials are used when an agent authenticates directly with each system using its own identity and preconfigured permissions. IAM users with long-lived keys are a weaker security design. Authorization code is user-delegated and interactive, so it does not fit a no-user-interaction autonomous agent workflow.

It is the fastest when it comes to rollback and deploying changes every hour

The best combination is A, D, and E because it replaces self-managed components with managed AWS services. AWS IoT Core is purpose-built for MQTT-based device communication, and IoT rules can invoke Lambda functions when messages arrive, allowing serverless processing of device data. Amazon DocumentDB is compatible with MongoDB workloads and removes the operational burden of running MongoDB clusters on EC2. For report generation, AWS Step Functions can orchestrate Lambda tasks, and the reports can be written to Amazon S3 and served publicly through CloudFront. The job duration of 120–600 seconds fits within Lambda’s 15-minute maximum invocation timeout. EKS and EC2-based MongoDB would increase operational overhead, while having Lambda poll IoT devices directly is a poor design compared with device publishing through AWS IoT Core. (AWS Documentation)

===============

The combination of changes that will meet the requirement with the least operational overhead are:

A. Deploy the API to multiple Regions. Configure Amazon Route 53 with custom domain names that route traffic to each Regional API endpoint.Implement a Route 53 multivalue answer routing policy.

B. Create a new KMS multi-Region customer managed key. Create a new KMS customer managed replica key in each in-scope Region.

C. Replicate the existing Secrets Manager secret to other Regions. For each in-scope Region’s replicated secret, select the appropriate KMS key.

These changes will enable the company to have an active-active configuration for its API across multiple Regions, while minimizing the complexity and cost of managing the secrets and keys.

A. This change will allow the company to use Route 53 to distribute traffic across multiple Regional API endpoints, based on the availability and latency of each endpoint.This will improve the performance and availability of the API for global customers12

B. This change will allow the company to use KMS multi-Region keys, which are KMS keys in different Regions that can be used interchangeably.This will simplify the encryption and decryption of secrets across Regions, as the same key material and key ID can be used in any Region34

C. This change will allow the company to use Secrets Manager replication, which replicates the encrypted secret data and metadata across the specified Regions.This will ensure that the secrets are consistent and accessible in any Region, andthat any update made to the primary secret will be propagated to the replica secrets automatically56

https://docs.aws.amazon.com/elasticloadbalancing/latest/application/load-balancer-access-logs.html

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_inheritance_auth.html

The company should use AWS Amplify to create a static website for uploads of media files. The company should use Amplify Hosting to serve the website through Amazon CloudFront. The company should use Amazon S3 to store the uploaded media files. The company should use Amazon Cognito to authenticate users. This solution will meet the requirements with the least operational overhead because AWS Amplify is a complete solution that lets frontend web and mobile developers easily build, ship, and host full-stack applications on AWS, with the flexibility to leverage the breadth of AWS services as use cases evolve. No cloud expertise needed1. By using AWS Amplify, the company can refactor the application to a serverless architecture that reduces operational complexity and costs. AWS Amplify offers the following features and benefits:

Amplify Studio: A visual interface that enables you to build and deploy a full-stack app quickly, including frontend UI and backend.

Amplify CLI: A local toolchain that enables you to configure and manage an app backend with just a few commands.

Amplify Libraries: Open-source client libraries that enable you to build cloud-powered mobile and web apps.

Amplify UI Components: Open-source design system with cloud-connected components for building feature-rich apps fast.

Amplify Hosting: Fully managed CI/CD and hosting for fast, secure, and reliable static and server-side rendered apps.

By using AWS Amplify to create a static website for uploads of media files, the company can leverage Amplify Studio to visually build a pixel-perfect UI and connect it to a cloudbackend in clicks. By using Amplify Hosting to serve the website through Amazon CloudFront, the company can easily deploy its web app or website to the fast, secure, and reliable AWS content delivery network (CDN), with hundreds of points of presence globally. By using Amazon S3 to store the uploaded media files, the company can benefit from a highly scalable, durable, and cost-effective object storage service that can handle any amount of data2. By using Amazon Cognito to authenticate users, the company can add user sign-up, sign-in, and access control to its web app with a fully managed service that scales to support millions of users3.

The other options are not correct because:

Using AWS Application Migration Service to migrate the application server to Amazon EC2 instances would not refactor the application or accelerate development. AWS Application Migration Service (AWS MGN) is a service that enables you to migrate physical servers, virtual machines (VMs), or cloud servers from any source infrastructure to AWS without requiring agents or specialized tools. However, this would not address the challenges of overutilization and data uploads failures. It would also not reduce operational overhead or costs compared to a serverless architecture.

Creating a static website for uploads of media files and using AWS AppSync to create an API would not be as simple or fast as using AWS Amplify. AWS AppSync is a service that enables you to create flexible APIs for securely accessing, manipulating, and combining data from one or more data sources. However, this would require more configuration and management than using Amplify Studio and Amplify Hosting. It would also not provide authentication features like Amazon Cognito.

Setting up AWS IAM Identity Center (AWS Single Sign-On) to give users the ability to sign in to the application would not be as suitable as using Amazon Cognito. AWS Single Sign-On (AWS SSO) is a service that enables you to centrally manage SSO access and user permissions across multiple AWS accounts and business applications. However, this service is designed for enterprise customers who need to manage access for employees or partners across multiple resources. It is not intended for authenticating end users of web or mobile apps.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-suspend-resume-processes.html

Using Amazon API Gateway with an SQS queue ensures all data sent to the API is durably stored for processing, even during traffic surges.

API Gateway service integration with SQS provides a managed, serverless ingestion layer that absorbs unpredictable traffic bursts without data loss.

The AWS Lambda function processes messages from SQS, providing scalable, on-demand compute to handle environmental data efficiently.

This solution aligns with AWS best practices for building resilient, serverless, event-driven architectures that can handle high-volume, bursty workloads.

The company should create a new AWS WAF web ACL. The company should add a new rule that blocks requests that match the SQL database rule group. The company should set the web ACL to allow all other traffic that does not match those rules. The company should attach the web ACL to the ALB in front of the ECS tasks. This solution will meet the requirements because AWS WAF is a web application firewall that lets you monitor and control web requests that are forwarded to your web applications. You can use AWS WAF to define customizable web security rules that control which traffic can access your web applications and which traffic should be blocked1. By creating a new AWS WAF web ACL, the company can create a collection of rules that define the conditions for allowing or blocking web requests. By adding a new rule that blocks requests that match the SQL database rule group, the company can prevent SQL injection attacks from reaching the ECS API service. The SQL database rule group is a managed rule group provided by AWS that contains rules to protect against common SQL injection attack patterns2. By setting the web ACL to allow all other traffic that does not match those rules, the company can ensure that legitimate traffic can access the API service. By attaching the web ACL to the ALB in front of the ECS tasks, the company can apply the web security rules to all requests that are forwarded by the load balancer.

The other options are not correct because:

Creating a new AWS WAF Bot Control implementation would not prevent SQL injection attacks from reaching the ECS API service. AWS WAF Bot Control is a feature that gives you visibility and control over common and pervasive bot traffic that can consume excess resources, skew metrics, cause downtime, or perform other undesired activities. However, it does not protect against SQL injection attacks, which are malicious attempts to execute unauthorized SQL statements against your database3.

Creating a new AWS WAF web ACL to monitor the HTTP requests and HTTPS requests that are forwarded to the ALB in front of the ECS tasks would not prevent SQL injection attacks from reaching the ECS API service. Monitoring mode is a feature that enables you to evaluate how your rules would perform without actually blocking any requests. However, this mode does not provide any protection against attacks, as it only logs and counts requests that match your rules4.

Creating a new AWS WAF web ACL and creating a new empty IP set in AWS WAF would not prevent SQL injection attacks from reaching the ECS API service. An IP set is a feature that enables you to specify a list of IP addresses or CIDR blocks that you want to allow or block based on their source IP address. However, this approach would not be effective or efficient against SQL injection attacks, as it would require constantly updating the IP set with new IP addresses of attackers, and it would not block attackers who use proxies or VPNs.

Option B effectively addresses the disaster recovery requirements:

Creating a new backup vault in both the source and recovery accounts allows for organized and secure storage of backups.

Using a multi-Region customer managed KMS key ensures that the backups are encrypted at all times and can be decrypted in the recovery account, maintaining data security during cross-account and cross-Region transfers.

Redirecting backups to the new backup vault in the source account and configuring a key policy to allow access from the recovery account facilitates seamless and secure backup copying.

Scheduling a cross-account backup plan enables automated and regular copying of backups to the recovery account, ensuring that the DR plan is consistently maintained.

This approach ensures that backups are securely and efficiently replicated to the recovery account in a different Region, aligning with the company ' s DR requirements.

The awsvpc network mode provides each task with its own elastic network interface (ENI) and a primary private IP address1. By using this network mode, the solutions architect can isolate the tasks from each other and apply security groups to the tasks directly2. This way, the solutions architect can control the inbound and outbound traffic at the task level and enforce the least privilege principle3. IAM roles for tasks allow the solutions architect to assign permissions to each task separately, so that they can access other AWS resources that they need4. By using IAM roles for tasks, the solutions architect can avoid passing IAM credentials into the container at launch time, which is less secure and more prone to errors5.

awsvpc network mode

Task networking with the awsvpc network mode

Security groups for your VPC

IAM roles for tasks

Best practices for managing AWS access keys

https://aws.amazon.com/about-aws/whats-new/2022/02/amazon-cloudfront-managed-prefix-list/

Amazon Elastic Container Service (ECS) on AWS Fargate is a fully managed service that allows you to run containers without having to manage the underlying infrastructure. When you launch tasks on Fargate, resources are automatically allocated based on the number of tasks running, which reduces the operational overhead.

Using ECS on Fargate allows you to assign custom tags to tasks using the --tags option in the run-task command, as described in the documentation:https://docs.aws.amazon.com/cli/latest/reference/ecs/run-task.htmlYou can also set enableECSManagedTags to true, which allows the service to automatically add the cluster name and service name as tags.https://docs.aws.amazon.com/AmazonECS/latest/developerguide/task-placement-constraints.html#tag-based-scheduling

This solution will meet the requirements with the least operational overhead because it allows the company to modify the existing environment ' s capacity configuration, so it becomes a load-balanced environment type. By selecting all availability zones, the company can ensure that the application is running in multiple availability zones, which can help to improve the availability and scalability of the application. The company can also add a scale-out rule that will run if the average CPU utilization is over 85% for 5 minutes, which can help to mitigate the performance issues. This solution does not require creating new Elastic Beanstalk environments or rebuilding the existing one, which reduces the operational overhead.

You can refer to the AWS Elastic Beanstalk documentation for more information on how to use this service:https://aws.amazon.com/elasticbeanstalk/You can refer to the AWS documentation for more information on how to use autoscaling:https://aws.amazon.com/autoscaling/

https://joe.blog.freemansoft.com/2020/04/protect-data-in-cloud-with-s3-access.html

This option allows the solutions architect to use a private hosted zone to host DNS records that are only accessible within the VPC1. By associating the private hosted zone with the VPC, the solutions architect can ensure that DNS queries from the VPC are routed to the private hosted zone2. By activating the enableDnsSupport attribute and the enableDnsHostnames attribute for the VPC, the solutions architect can enable DNS resolution and hostname assignment for instances in the VPC3. By creating a new VPC DHCP options set, and configuring domain-name-servers=AmazonProvidedDNS, the solutions architect can use Amazon-provided DNS servers to resolve DNS queries from instances in the VPC4. By associating the new DHCP options set with the VPC, the solutions architect can apply the DNS settings to all instances in the VPC5.

What is Amazon Route 53 Resolver?

Associating a private hosted zone with your VPC

Using DNS with your VPC

DHCP options sets

Modifying your DHCP options

The performance symptom is classic Lambda cold-start variability. The API is used sporadically, so Lambda execution environments may be recycled between requests. The first request takes longer because Lambda must initialize an execution environment; subsequent identical requests are faster because the environment is already warm. Provisioned concurrency keeps execution environments initialized and ready to respond, producing more consistent latency. AWS Application Auto Scaling can manage provisioned concurrency with target tracking, which helps align provisioned capacity with usage patterns. DynamoDB on-demand mode already scales automatically for table throughput, and DAX would mainly help read-heavy caching scenarios, not Lambda initialization delay. CloudFront may reduce network latency for cacheable content, but it does not remove Lambda cold starts for dynamic API calls. (AWS Documentation)

===============

Deploy AWS DataSync Agent:

Install the DataSync agent on your on-premises environment. This can be done by downloading the agent as a virtual appliance and deploying it on VMware ESXi, Hyper-V, or KVM hypervisors.

Configure Source and Destination Locations:

Set up the source location pointing to your on-premises storage where the software images are currently stored.

Configure the destination location to point to your Amazon S3 bucket in the ap-northeast-1 Region.

Create and Schedule DataSync Tasks:

Create a DataSync task to automate the transfer process. This task will specify the source and destination locations and set options for how the data should be transferred.

Schedule the task to run at intervals that suit your data transfer requirements, ensuring new images are transferred as they are created.

Encryption in Transit:

AWS DataSync automatically encrypts data in transit using TLS, ensuring that your data is secure during the transfer process.

Monitoring and Management:

Use the DataSync console or the AWS CLI to monitor the progress of your data transfers and manage the tasks.

AWS DataSync is an efficient solution that automates and accelerates the process of transferring large amounts of data to AWS, handling encryption, data integrity checks, and optimizing network usage without requiring custom development.

References

AWS Storage Blog on DataSync【40】.

AWS DataSync Documentation【41】.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/high_availability_origin_failover.html

The correct answer is B.

B. This solution is the most cost-effective because it uses an EC2 Instance Savings Plan for the online gaming application instances, which provides the lowest prices and savings up to 72% compared to On-Demand prices. The EC2 Instance Savings Plan applies to any instance size within the same family and region, regardless of availability zone, operating system, or tenancy. The online gaming application instances run all year and must always be available, so they are not suitable for Spot Instances, which can be interrupted with a two-minute notice. This solution also uses Spot Instances for the analytics application, which can reduce the cost by up to 90% compared to On-Demand prices. The analytics application can be interrupted and resumed without issue, so it is a good fit for Spot Instances, which use spare EC2 capacity.This solution does not require AWSService Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts123

A. This solution is incorrect because it uses On-Demand Instances for the analytics application, which are more expensive than Spot Instances. The analytics application can be interrupted and resumed without issue, so it can benefit from the lower cost of Spot Instances, which use spare EC2 capacity.

C. This solution is incorrect because it uses Spot Instances for the online gaming application, which can be interrupted with a two-minute notice. The online gaming application instances must always be available, so they are not suitable for Spot Instances, which use spare EC2 capacity. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

D. This solution is incorrect because it uses On-Demand Instances for the online gaming application, which are more expensive than an EC2 Instance Savings Plan. The online gaming application instances run all year and must always be available, so they are suitable for an EC2 Instance Savings Plan, which provides the lowest prices and savings up to 72% compared to On-Demand prices. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

The requirement is preventive enforcement during CloudFormation provisioning. AWS CloudFormation Hooks are designed to evaluate CloudFormation and Cloud Control API operations before resources are created or updated. If a resource is noncompliant, a Hook can fail the operation and prevent provisioning. Lambda Hooks allow custom logic, which fits predefined company-specific rules. Deploying and protecting Hooks centrally with StackSets, SCPs, and IAM policies gives consistent enforcement across the organization with low operational overhead. AWS Config custom rules are detective controls; they evaluate resources after deployment and cannot reliably prevent initial creation. CloudFormation macros require templates to explicitly use the macro and are not a universal enforcement layer. Manually invoking CloudFormation Guard through a Lambda function does not automatically block every CloudFormation deployment unless separately integrated.

The current Redshift configuration uses eight ra3.4xlarge nodes with on-demand pricing and is paused outside an 8-hour daily window. The ingestion workload runs only 3 hours per day and has about 85% CPU utilization during ingestion, which indicates that the existing node size and count are adequate for performance. The main opportunity is cost optimization for a workload that is time-bound and does not require a continuously running provisioned cluster.

Amazon Redshift Serverless is designed for such intermittent or variable workloads. With Redshift Serverless, you configure compute capacity in terms of Redshift Processing Units (RPUs) and pay per use based on the RPU-hours consumed when queries and data ingestion jobs are actually running. When there is no activity, there is no compute charge. Redshift Serverless can also be created from an existing provisioned Redshift snapshot, allowing an easy migration path from a node-based cluster to a serverless endpoint without re-ingesting or manually moving data.

Option C creates a new Redshift Serverless endpoint with 64 RPUs from the most recent snapshot. This takes advantage of snapshots that already exist on the current cluster. The internal applications are then updated to read from the new serverless endpoint, and the existing cluster is deleted to stop incurring node-based charges. For a workload that runs only a few hours per day, moving to serverless compute is usually more cost-effective than maintaining a provisioned cluster, even if that cluster is paused outside the ingestion window, because serverless eliminates the need to manage cluster sizing and pausing and optimizes billing around actual usage.

Option A proposes staying on provisioned RA3 nodes and additionally purchasing 1-year Reserved Nodes. Reserved Nodes apply discount to node hours whether or not the cluster is paused or fully utilized, and they are designed to be cost-effective for steady, predictable, long-running workloads. For an 8-hour-per-day workload, especially with only 3 hours of intensive ingestion, Reserved Nodes are likely to be less cost-optimal than an on-demand, usage-based serverless model. Also, concurrency scaling is primarily for handling bursty query concurrency, not for cost reduction of a regular ingestion workload.

Option B replaces the existing eight ra3.4xlarge nodes with six ra3.16xlarge nodes and enables auto scaling. ra3.16xlarge provides significantly more capacity per node and is generally more expensive. The original workload already runs at acceptable utilization (about 85% during ingestion), so scaling up to larger nodes is not necessary for performance. Adding auto scaling on top of this likely increases costs and complexity without a clear benefit for a predictable, time-bound daily ingestion pattern.

Option D uses Redshift Spectrum and unloads data from the cluster to Amazon S3 for querying. Redshift Spectrum allows querying data stored in S3 using external tables from within a Redshift cluster, and is charged per amount of data scanned. However, the option still depends on maintaining a Redshift cluster and does not fundamentally optimize the compute cost of the ingestion workload. It also changes query patterns and data access architecture for the internal applications and may increase query cost if large volumes of S3 data are scanned by Spectrum.

Therefore, using Redshift Serverless created from the existing snapshot and paying only for compute usage during ingestion and queries, as described in option C, is the best way to optimize cost for this specific workload pattern.

This option allows the solutions architect to use Aurora global database to replicate data across multiple AWS Regions with low latency and high availability1. By launching the solution in the target Region, the solutions architect can ensure that the API Gateway, Lambda functions, and other resources are ready to serve traffic in case of a disaster in the source Region. By configuring the two Regional solutions to work in an active-passive configuration, the solutions architect can minimize costs and avoid data conflicts by having only one primary Region that accepts write operations and one secondary Region that serves as a standby2. The RTO and RPO requirements can be met by using Aurora global database, which supports sub-second failover times and near real-time replication1.

Working with Amazon Aurora global database

Active-passive failover

The cleanest design is an AWS Config organization conformance pack deployed centrally. Conformance packs are YAML templates that can include AWS Config managed rules, custom rules, and remediation actions. AWS Config also supports managing conformance packs across all member accounts in an AWS Organizations organization, allowing centralized deployment, update, and deletion. The PutOrganizationConformancePack API deploys a conformance pack across member accounts and can create AWS Config rules and remediation actions without requiring member-account IAM permissions for those individual rule operations. Because the requirement includes four custom rules and five managed rules, the conformance pack must include all nine rules. Deploying only custom rules leaves the managed rules outside the update model. CloudFormation StackSets add unnecessary operational overhead and do not provide the same native organization conformance-pack governance.

https://docs.aws.amazon.com/firehose/latest/dev/creating-the-stream-to-splunk.html

D is correct because this solution modernizes the application into aserverless architectureusing API Gateway and Lambda for scalable microservices.Aurora Serverless v2supports SQL workloads and auto-scales based on demand.AWS SCTallows migration of SQL Server stored procedures to Aurora-compatible formats. This setup ensures cost efficiency, scalability, and minimal manual intervention.

A rehosts but doesn’t refactor into microservices.

B uses MySQL, which might not support the SQL Server-specific stored procedures fully.

C adds unnecessary complexity and loses relational database functionality by using DAX.

To set up a STARTTLS connection, the SMTP client connects to the Amazon SES SMTP endpoint on port 25, 587, or 2587, issues an EHLO command, and waits for the server toannounce that it supports the STARTTLS SMTP extension. The client then issues the STARTTLS command, initiating TLS negotiation. When negotiation is complete, the client issues an EHLO command over the new encrypted connection, and the SMTP session proceeds normally To set up a TLS Wrapper connection, the SMTP client connects to the Amazon SES SMTP endpoint on port 465 or 2465. The server presents its certificate, the client issues an EHLO command, and the SMTP session proceeds normally.

https://docs.aws.amazon.com/ses/latest/dg/smtp-connect.html

AWS Audit Manager is the managed service built for continuous audit evidence collection and audit-ready reporting. It can automatically collect evidence from AWS accounts and services, map evidence to controls and frameworks, and help generate reports for auditors. AWS Config provides the technical compliance signal for resource configuration, and conformance packs package managed or custom AWS Config rules into repeatable compliance templates. An AWS Config aggregator centralizes visibility across accounts. Option D therefore combines control evaluation, centralized aggregation, framework alignment, historical evidence, and audit reporting without custom pipelines. CloudTrail plus Athena or Config snapshots plus Lambda would require custom report generation. Security Hub is useful for security posture aggregation, but the question specifically requires evidence collection and auditor-ready reports mapped to regulatory frameworks.

The key requirement is to provide access to a private Git repository while keeping the SageMaker notebook instances isolated from the internet. The environment is intentionally configured without internet access, and connectivity to AWS services is provided through VPC endpoints. Introducing a NAT gateway and routing to the internet would violate the requirement to keep the notebook instances isolated from the internet, and network ACLs cannot reliably restrict outbound access by URL or domain name because NACLs operate at the IP and port level and do not provide DNS-aware URL filtering.

SageMaker supports integrating Git repositories so that notebooks can pull code directly as part of the workflow. When using a private repository, credentials must be handled securely. Using AWS Secrets Manager to store and reference Git credentials allows authentication without embedding usernames and passwords in code or URLs and without granting broad network access. This approach meets the requirement with low operational overhead because it relies on managed SageMaker Git integration and managed secret storage rather than custom networking controls.

Option A uses SageMaker’s Git repository integration with the remote URL and stores credentials in AWS Secrets Manager. This allows notebooks to access the private repository in a controlled, auditable way while preserving the “no internet access” posture of the VPC design (because it does not require adding a NAT gateway or public routes).

Option B is not appropriate because embedding credentials (even just usernames) in URLs is not a secure or robust credential management practice. It also does not solve private authentication in a secure manner and can lead to credential leakage through logs or configuration.

Options C and D are not correct because they require adding a NAT gateway and routing to the internet. That directly conflicts with the requirement that SageMaker notebook instances remain isolated from the internet. Additionally, the proposed restriction mechanism is invalid: network ACLs cannot restrict traffic to a specific URL. They only allow/deny traffic based on IP addresses, ports, and protocols. A Git repository can resolve to multiple IPs, can change IPs, and can use CDNs, making NACL-based IP allowlisting brittle and operationally heavy.

Therefore, integrating the Git repository with SageMaker and using Secrets Manager for credentials is the best solution with the least operational overhead while maintaining internet isolation.

C is correct because:

AWS WAF integrates natively with API Gateway and protects against common web exploits (e.g., SQL injection, XSS).

Amazon Inspector can scan the legacy EC2 instance for known vulnerabilities.

Amazon GuardDuty is a continuous security monitoring service that detects threats butdoes not blocktraffic (B and D are incorrect because GuardDuty doesn’t block).

Transfer acceleration. S3 Transfer Acceleration utilizes the Amazon CloudFront global network of edge locations to accelerate the transfer of data to and from S3 buckets. By enabling S3 Transfer Acceleration on the centralized S3 bucket, the users in Europe will experience faster uploads as their data will be routed through the closest CloudFront edge location.

Set Up AWS Elastic Disaster Recovery:

Navigate to the AWS Elastic Disaster Recovery (DRS) console.

Configure the Elastic Disaster Recovery service to replicate your on-premises VMware vSphere VMs to Amazon EC2 instances. This involves installing the AWS Replication Agent on your VMs.

Configure Replication Settings:

Define the replication settings, including the Amazon EC2 instance type and the Amazon EBS volume configuration. Ensure that the replication frequency meets your Recovery Point Objective (RPO) of 5 minutes.

Monitor Data Replication:

Monitor the initial data replication process in the Elastic Disaster Recovery console. Once the initial sync is complete, the status should show as " Healthy " indicating that the data replication is up-to-date and within the RPO requirements.

Disaster Recovery (Failover):

In the event of a disaster, initiate a failover from the Elastic Disaster Recovery console. This will launch the replicated Amazon EC2 instances using the Amazon EBS volumes with the latest data.

Failback Process:

Once the on-premises environment is restored, perform a failback operation to synchronize the data from AWS back to your on-premises VMware environment. Use the failback client provided by AWS Elastic Disaster Recovery to ensure data consistency and minimal downtime during the failback process.

Using AWS Elastic Disaster Recovery provides a low-overhead, automated solution for disaster recovery that ensures minimal data loss and meets the RPO requirement of 5 minutes​(Amazon Web Services, Inc.)​​(AWS Documentation)​.

Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.

Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.

Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB. Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.

Auto Scaling groups

DynamoDB auto scaling

AWS Lambda

DynamoDB global tables

AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform

Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

AWS Aurora Global Database vs. DynamoDB Global Tables

Amazon Simple Queue Service (SQS)

https://docs.aws.amazon.com/vm-import/latest/userguide/vmimport-image-import.html

- Export an OVF Template

- Create / use an Amazon S3 bucket for storing the exported images. The bucket must be in the Region where you want to import your VMs.

- Create an IAM role named vmimport.

- You ' ll use AWS CLI to run the import commands.

https://aws.amazon.com/premiumsupport/knowledge-center/import-instances/

The requirement says replication must use a private dedicated connection, so AWS Direct Connect with a Direct Connect gateway is the correct network path, not Site-to-Site VPN. AWS Elastic Disaster Recovery supports data routing and throttling controls; by default, replication can use the public internet, but selecting private IP data replication routes replication traffic privately. Bandwidth throttling is also part of Elastic Disaster Recovery’s data routing and throttling configuration, which directly addresses the 75% bandwidth limit. The target VPC should use private subnets and a virtual private gateway so the recovery environment is not exposed publicly. Public subnets and an internet gateway violate the requirement. Matching the recovery instance’s private IP is a launch/failover concern, not the correct control for replication traffic routing and throttling.

Dis correct:Aurora PostgreSQLis afully managed, high-availabilitydatabase engine.

UseAWS DMSto migrate data andAWS Schema Conversion Tool (SCT)to convert stored procedures from Oracle to Aurora-compatible code.

This meets the requirements ofminimal downtime,managed services, andstored procedure support.

Ais not valid—RDS doesn’t allow custom S3 integration for storage.

Binvolves EC2, which violates the “managed services” constraint.

Crequires complete rewrite of the application logic, which contradicts the minimal downtime requirement.

The workload uses EC2 Spot Instances, which are subject to capacity availability constraints. Using a single instance type significantly increases the risk of launch failures when Spot capacity for that instance type is constrained. This directly impacts application reliability and increases user wait times.

Attribute-based instance type selection allows Auto Scaling to choose from multiple compatible instance types based on defined requirements such as vCPU count, memory, and architecture. This improves the probability that Spot capacity is available at launch time because Auto Scaling can select from a broader pool of instance types instead of a single one.

Option D correctly applies attribute-based instance type selection within a new version of the existing launch template. Auto Scaling groups are designed to work with launch templates, and updating the template version is the modern and recommended approach. This change increases Spot placement flexibility and reliability without altering application behavior or introducing additional infrastructure.

Option A is incorrect because launch configurations are an older construct and do not support attribute-based instance type selection. They are also not recommended for new or updated Auto Scaling designs.

Option B does not address the root cause of Spot launch failures. Simply using a larger instance type may actually reduce available Spot capacity and worsen reliability.

Option C increases the number of placement groups, but placement groups do not solve Spot capacity shortages. They influence instance placement for networking or latency characteristics, not Spot availability across instance types.

Therefore, updating the launch template to use attribute-based instance type selection is the most effective way to improve reliability for a Spot-based Auto Scaling workload.

https://docs.aws.amazon.com/sns/latest/dg/sns-cross-region-delivery.html

The company should purchase the same Reserved Instances for an additional 3-year term with All Upfront payment. The company should purchase a 3-year Compute Savings Plan with All Upfront payment in the management account to cover any additional compute costs. This solution will provide the company with the most cost savings because Reserved Instances and Savings Plans are both pricing models that offer significant discounts compared to On-Demand pricing. Reserved Instances are commitments to use a specific instance type and size in a single Region for a one- or three-year term. You can choose between three payment options: No Upfront, Partial Upfront, or All Upfront. The more you pay upfront, the greater the discount1. Savings Plans are flexible pricing models that offer low prices on EC2 instances, Fargate, and Lambda usage, in exchange for a commitment to a consistent amount of usage (measured in $/hour) for a one- or three-year term. You can choose between two types of Savings Plans: Compute Savings Plans and EC2 Instance Savings Plans. Compute Savings Plans apply to any EC2 instance regardless of Region, instance family, operating system, or tenancy, including those that are part of EMR, ECS, or EKS clusters, or launched by Fargate or Lambda. EC2 Instance Savings Plans apply to a specific instance family within a Region and provide the most savings2. By purchasing the same Reserved Instances for an additional 3-year term with All Upfront payment, the company can lock in the lowest possible price for its EC2 instances that run continuously for 3 years. By purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account, the company can benefit from additional discounts on any other compute usage across its member accounts.

The other options are not correct because:

Purchasing a 1-year Compute Savings Plan with No Upfront payment in each member account would not provide as much cost savings as purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account. A 1-year term offers lower discounts than a 3-year term, and a No Upfront payment option offers lower discounts than an All Upfront payment option. Also, purchasing a Savings Plan in each member account would not allow the company to share the benefits of unused Savings Plan discounts across its organization.

Purchasing a 3-year EC2 Instance Savings Plan with No Upfront payment in the management account to cover EC2 costs in each AWS Region would not provide as much cost savings as purchasing Reserved Instances for an additional 3-year term with All Upfront payment. An EC2 Instance Savings Plan offers lower discounts than Reserved Instances for the same instance family and Region. Also, a No Upfront payment option offers lower discounts than an All Upfront payment option.

Purchasing a 3-year EC2 Instance Savings Plan with All Upfront payment in each member account would not provide as much flexibility or cost savings as purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account. An EC2 Instance Savings Plan applies only to a specific instance family within a Region and does not cover Fargate or Lambda usage. Also, purchasing aSavings Plan in each member account would not allow the company to share the benefits of unused Savings Plan discounts across its organization.

" Save your custom error pages in a location that is accessible to CloudFront. We recommend that you store them in an Amazon S3 bucket, and that you don’t store them in the same place as the rest of your website or application’s content. If you store the custom error pages on the same origin as your website or application, and the origin starts to return 5xx errors, CloudFront can’t get the custom error pages because the origin server is unavailable. " https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/GeneratingCustomErrorResponses.html

Auto Scaling Group with Application Load Balancer:

Moving the Tomcat server to an Auto Scaling group ensures that the number of instances adjusts dynamically based on the traffic load. An Application Load Balancer (ALB) distributes incoming traffic across multiple instances, improving the application ' s reliability and availability.

Migrate to Amazon Aurora with Replica:

Migrating the MySQL database to Amazon Aurora and adding an Aurora Replica enhances the database ' s scalability and availability. Aurora is optimized for performance, and replicas help distribute read traffic, reducing the load on the primary instance.

Additional Public Subnet:

Creating an additional public subnet in a different Availability Zone enhances fault tolerance. This ensures that the website remains accessible even if one Availability Zone experiences issues.

References

AWS Well-Architected Framework

Amazon Aurora Documentation

In this scenario, the Amazon EC2 instances are in an Auto Scaling group already which means that the database read operations is the possible bottleneck especially during the month-end wherein the reports are generated. This can be solved by creating RDS read replicas.

https://aws.amazon.com/blogs/compute/implementing-canary-deployments-of-aws-lambda-functions-with-alias-traffic-shifting/

https://docs.aws.amazon.com/lambda/latest/dg/configuration-aliases.html

The correct answer is A, D, and F. AWS AppFabric is designed to ingest and normalize audit logs from compatible SaaS applications into the Open Cybersecurity Schema Framework, which makes logs from different SaaS providers easier to analyze consistently. Amazon Security Lake centralizes security data from AWS sources and custom sources, including AppFabric. Therefore, SaaS audit logs, CloudTrail events, Amazon S3 data events, Amazon EKS audit logs, and VPC Flow Logs can be centralized in Security Lake. OpenSearch Ingestion can then move Security Lake data into Amazon OpenSearch Service, where dashboards can visualize user activity, IP addresses, email addresses, and access frequencies. Options B and C are weaker because they create custom log pipelines and dashboards instead of using managed security data normalization. Option E uses Redshift and QuickSight, but OpenSearch is the better fit for security investigation dashboards.

===============

Establish AWS Direct Connect Connections:

Step 1: Set up two AWS Direct Connect (DX) connections from the company headquarters to a chosen AWS Region. This provides a redundant and high-availability setup to ensure continuous connectivity.

Step 2: Ensure that these DX connections terminate in a specific Direct Connect location associated with the chosen AWS Region.

Use Company WAN:

Step 1: Configure the company ' s global WAN to route traffic through the established Direct Connect connections.

Step 2: This setup ensures that all traffic between the company ' s headquarters and AWS does not traverse the public internet, maintaining compliance with security requirements.

Set Up Direct Connect Gateway:

Step 1: Create a Direct Connect Gateway in the AWS Management Console. This gateway allows you to connect your Direct Connect connections to multiple VPCs across different AWS Regions.

Step 2: Associate the Direct Connect Gateway with the VPCs in the various Regions where your critical data is stored. This enables access to data in multiple Regions through a single Direct Connect connection.

By using Direct Connect and Direct Connect Gateway, the company can achieve secure, reliable, and cost-effective access to data stored across multiple AWS Regions without using the public internet, ensuring compliance with industry regulations.

References

AWS Direct Connect Documentation

Building a Scalable and Secure Multi-VPC AWS Network Infrastructure​(AWS Documentation)​​(AWS Documentation)​.

" A usage plan specifies who can access one or more deployed API stages and methods—and also how much and how fast they can access them. The plan uses API keys to identify API clients and meters access to the associated API stages for each key. It also lets you configure throttling limits and quota limits that are enforced on individual client API keys. " https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-api-usage-plans.html

A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action on IPs with rates that go over a limit. You set the limit as the number of requests per 5-minute time span...... The following caveats apply to AWS WAF rate-based rules: The minimum rate that you can set is 100. AWS WAF checks the rate of requests every 30 seconds, and counts requests for the prior five minutes each time. Because of this, it ' s possible for an IP address to send requests at too high a rate for 30 seconds before AWS WAF detects and blocks it. AWS WAF can block up to 10,000 IP addresses. If more than 10,000 IP addresses send high rates of requests at the same time, AWS WAF will only block 10,000 of them. " https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-type-rate-based.html

https://aws.amazon.com/premiumsupport/knowledge-center/s3-access-denied-error-kms/

" An error occurred (AccessDenied) when calling the PutObject operation: Access Denied " This error message indicates that your IAM user or role needs permission for the kms:GenerateDataKey action.

GitHub Webhooks to Trigger CodePipeline:

Configure GitHub webhooks to trigger the AWS CodePipeline pipeline. This ensures that every code push to the repository automatically triggers the pipeline, initiating the build and deployment process.

Unit Testing with Jenkins and AWS CodeBuild:

Use Jenkins integrated with the AWS CodeBuild plugin to perform unit testing. Jenkins will manage the build process, and the results will be handled by CodeBuild.

Notifications for Bad Builds:

Configure Amazon SNS (Simple Notification Service) to send alerts for any failed builds. This keeps the engineering team informed of build issues immediately, allowing for quick resolutions.

Blue/Green Deployment with AWS CodeDeploy:

Utilize AWS CodeDeploy with a blue/green deployment strategy. This method reduces downtime and risk by running two identical production environments (blue and green). CodeDeploy shifts traffic between these environments, allowing you to test in the new environment (green) while the old environment (blue) remains live. If issues arise, you can quickly roll back to the previous environment.

This solution provides seamless, zero-downtime deployments, and the ability to quickly roll back changes if necessary, fulfilling the requirements of the startup company.

References

AWS DevOps Blog on Integrating Jenkins with AWS CodeBuild and CodeDeploy【32】.

Plain English Guide to AWS CodePipeline with GitHub【33】.

Jenkins Plugin for AWS CodePipeline【34】.

A: Use dynamic IAM policies with {aws:PrincipalTag/userName} to enforceprefix-level access control— i.e., bucket/userA/*, bucket/userB/*.

C: Enable CloudTraildata eventsto capture object-level access andquery them withAthena. This is the AWS-recommended way to audit per-user object access.

Incorrect:

B doesn ' t provide user isolation.

D only capturesmanagement events, not object-level data access.

E is legacy, inefficient, and not structured for per-user auditing.

C is correct because it converts the single–Availability Zone, single-EC2-instance ECS design into a managed, multi-AZ, self-healing architecture with minimal day-to-day operations. The current design has multiple single points of failure: one EC2 instance for ECS capacity and one Availability Zone for all components. Moving the ECS service to AWS Fargate removes the need to manage EC2 instances (capacity provisioning, patching, and scaling of the container instances) and allows the service to run tasks across multiple Availability Zones for higher availability. On the database side, modifying Aurora PostgreSQL to a Multi-AZ DB cluster (by adding a replica in another AZ) increases availability and supports faster recovery from an AZ failure with AWS-managed failover.

Why the other options are less suitable:

A: Making Aurora Multi-AZ improves database availability, but it does not address the compute layer’s biggest issue: ECS is on a single EC2 instance in one AZ with no auto scaling. RDS Proxy can help with connection management, but it does not fix the application’s single-AZ ECS single-instance availability risk.

B: Cross-Region read replicas and manual failover scripts increase operational burden. Also, it keeps ECS on EC2 (still requires instance management) and introduces a manual failover process, which is the opposite of “least operational overhead.”

D: Multi-Region active-active plus Aurora global database can deliver very high availability, but it adds significant complexity (multi-Region deployment, routing strategy, global database considerations, operational procedures). That is higher operational overhead than a straightforward multi-AZ design using managed services.

https://aws.amazon.com/premiumsupport/knowledge-center/cross-account-access-s3/

C is correct: AWS Transfer Family can store uploaded files directly into Amazon S3.S3 event notificationscan triggerAWS Glue jobsto transform the data. Upon successful transformation, Glue can send a message toAmazon SQS, enabling event-driven architecture with minimal management.

A and D involve cron jobs or scheduled Glue jobs, which increase operational overhead and delay.

B (EMR) is overkill for frequent, lightweight transformations.

The correct solution is to use the authenticate-oidc action for the ALB listener rule and configure it with the details of the AWS Directory Service for Microsoft Active Directory directory. This way, the ALB can use OpenID Connect (OIDC) to authenticate users against the directory and grant them access to the intranet web application. The app client in the directory is used to register the ALB as an OIDC client and provide the necessary credentials and endpoints. The callback URL is the URL that the ALB redirects the user to after a successful authentication. This solution does not require any additional services or roles, and it leverages the existing directory accounts for all users.

The other solutions are incorrect because they either use the wrong action for the ALB listener rule, or they involve unnecessary or incompatible services or roles. For example:

Solution B is incorrect because it uses Amazon Cognito user pool, which is a separate user directory service that does not integrate with AWS Directory Service for Microsoft Active Directory. To use this solution, the company would have to migrate or synchronize their users from the directory to the user pool, which is not required by the question. Moreover, the authenticate-cognito action for the ALB listener rule only works with Amazon Cognito user pools, not with federated identity providers (IdPs) that have metadata from the directory.

Solution C is incorrect because it uses IAM as an identity provider (IdP), which is not compatible with AWS Directory Service for Microsoft Active Directory. IAM can only be used as an IdP for web identity federation, which allows users to sign in with social media or other third-party IdPs, not with Active Directory. Moreover, the authenticate-oidc action for the ALB listener rule requires an OIDC IdP, not a SAML 2.0 federation IdP, which is what IAM provides.

Solution D is incorrect because it uses AWS IAM Identity Center (AWS Single Sign-On), which is a service that simplifies the management of SSO access to multiple AWS accounts and business applications. This service is not needed for the scenario in the question, which only involves a single intranet web application. Moreover, the authenticate-cognito action for the ALB listener rule does not work with external IdPs that use SAML, such as AWS IAM Identity Center.

Authenticate users using an Application Load Balancer

What is AWS Directory Service for Microsoft Active Directory?

Using OpenID Connect for user authentication

The existing application already runs in containers on a self-managed container platform, and compliance requires proprietary agent software on each container platform node. Amazon EKS with managed node groups is the best fit because it preserves the Kubernetes operating model and allows custom AMIs for worker nodes, where the agent can be pre-installed. This minimizes migration effort because Kubernetes manifests and container deployment patterns can remain largely intact. AWS DMS is appropriate for migrating the existing MySQL database to Amazon RDS for MySQL, and a Multi-AZ RDS deployment improves availability with less operational overhead than managing source-replica VMs. Option D is invalid because Fargate does not allow custom AMIs or node-level agents. Option A requires more self-management. Option C changes the application architecture too aggressively. (AWS Documentation)

===============

A and C are correct. Aurora PostgreSQL can store patient data in an encrypted database and can enforce TLS for encrypted connections in transit. For tamper-resistant auditing, Aurora Database Activity Streams are the stronger choice because they monitor database activity and send the stream to Amazon Kinesis for external consumption. AWS documentation states that activity streams monitor and report activities and that the activity stream is collected and transmitted to Kinesis. Sending those audit records to Amazon S3 and enabling S3 Object Lock in compliance mode provides immutable retention. Compliance mode prevents protected object versions from being overwritten or deleted by any user, including the root user, during the retention period. pgAudit logs in CloudWatch are useful, but they do not provide the same database activity stream control and immutability pattern.

 The company should configure a cross-Region read replica for the RDS database in the new Region. The company should change the Route 53 record to latency-based routing to connect to the API Gateway API. This solution will meet the requirements because a cross-Region read replica is a feature that enables you to create a MariaDB, MySQL, Oracle, PostgreSQL, or SQL Server read replica in a different Region from the source DB instance. You can use cross-Region read replicas to improve availability and disaster recovery, scale out globally, or migrate an existing database to a new Region1. By creating a cross-Region read replica for the RDS database in the new Region, the company can have a standby copy of its primary database that can serve read-only traffic from users in Europe. A latency-based routing policy is a feature that enables you to route traffic based on the latency between your users and your resources. You can use latency-based routing to route traffic to the resource that provides the best latency2. By changing the Route 53 record to latency-based routing, the company can minimize latency for users who download reports by connecting them to the API Gateway API in the Region that provides the best response time.

The other options are not correct because:

Using AWS Database Migration Service (AWS DMS) to replicate the primary database in the original Region to the database in the new Region would not be as cost-effective or simple as using a cross-Region read replica. AWS DMS is a service that enables you to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use AWS DMS to perform one-time migrations or continuous data replication with high availability and consolidate databases intoa petabyte-scale data warehouse3. However, AWS DMS requires more configuration and management than creating a cross-Region read replica, which is fully managed by Amazon RDS. AWS DMS also incurs additional charges for replication instances and tasks.

Creating an Amazon API Gateway Data API service integration with Amazon Redshift would not help with disaster recovery or minimizing latency. The Data API is a feature that enables you to query your Amazon Redshift cluster using HTTP requests, without needing a persistent connection or a SQL client. It is useful forbuilding applications that interact with Amazon Redshift, but not for replicating or recovering data from an RDS database.

Creating an AWS Data Exchange datashare by connecting AWS Data Exchange to the Redshift cluster would not help with disaster recovery or minimizing latency. AWS Data Exchange is a service that makes it easy for AWS customers to exchange data in the cloud. You can use AWS Data Exchange to subscribe to a diverse selection of third-party data products or offer your own data products to other AWS customers. A datashare is a feature that enables you to share live and secure access to your Amazon Redshift data across your accounts or with third parties without copying or moving the underlying data. It is useful for sharing query results and views with other users, but not for replicating or recovering data from an RDS database.

The current architecture uses a fleet of small EC2 instances that poll SQS for URLs and write results to EFS. Because URLs arrive only occasionally and each crawl takes 10 seconds or less, EC2 instances are often idle waiting for messages. This leads to unnecessary compute and storage costs.

This workload is naturally event-driven: each URL in the SQS queue triggers a short-lived crawl operation. AWS Lambda is well suited for such event-driven, short-duration tasks. Lambda integrates natively with Amazon SQS as an event source, which allows Lambda to poll the SQS queue automatically and invoke functions when messages arrive, scaling the concurrency up and down as needed. When there are no messages, there are no Lambda invocations and no compute charges, eliminating the cost of idle EC2 instances.

For storage of crawl results, Amazon S3 is a highly durable, cost-effective object store. The current use of an EFS file system mounted on all instances is no longer necessary if each Lambda invocation can write the output directly to S3 as objects (for example, CSV files). S3 charges only for the storage used and requests, and does not require continuously running file system infrastructure.

Option B replaces the idle fleet of EC2 instances with a Lambda function that reads from SQS. This aligns compute usage with actual work and takes advantage of serverless scaling and pricing.

Option E modifies the web-crawling process to store results in Amazon S3 instead of EFS, removing the need to maintain an EFS file system and its associated costs.

Option A increases instance size to m5.8xlarge, which greatly increases capacity and cost. Reducing the number of instances by 50% does not offset the large increase in instance size; it does not address idle capacity and likely increases overall cost.

Option C uses Amazon Neptune, which is a managed graph database service. It is not needed for storing simple CSV output; it is more complex and more expensive than S3 for this use case.

Option D uses Aurora Serverless MySQL. While Aurora Serverless can automatically scale, using a relational database to store simple crawl outputs adds unnecessary cost and operational complexity compared to S3 objects.

Therefore, moving the crawling logic to Lambda triggered by SQS (option B) and writing results directly to Amazon S3 (option E) meets the requirements in the most cost-effective way.

Bring your own IP addresses (BYOIP) You can bring part or all of your publicly routable IPv4 or IPv6 address range from your on-premises network to your AWS account. You continue toown the address range, but AWS advertises it on the internet by default. After you bring the address range to AWS, it appears in your AWS account as an address pool. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-byoip.html AWS Transfer for SFTP enables you to easily move your file transfer workloads that use the Secure Shell File Transfer Protocol (SFTP) to AWS without needing to modify your applications or manage any SFTP servers.https://aws.amazon.com/about-aws/whats-new/2018/11/aws-transfer-for-sftp-fully-managed-sftp-for-s3/

The correct answer is A, C, and F. The company wants to host a private DNS namespace, myvpc.example.com, inside AWS and allow on-premises devices to resolve records in that namespace. A Route 53 private hosted zone is the correct hosted zone type because the domain is intended for private VPC resolution, not public internet DNS. To allow DNS queries from the on-premises network into AWS, Route 53 Resolver inbound endpoints are required. The on-premises DNS resolvers then need conditional forwarding rules that forward queries for myvpc.example.com to the IP addresses of the inbound Resolver endpoint. An outbound endpoint is used when AWS resources need to resolve DNS names hosted on premises. That is not the stated requirement here. A public hosted zone would expose records publicly and is not appropriate for private VPC DNS.

===============

This solution uses a combination of AWS Resource Access Manager (RAM) and automated backups to migrate the Lambda functions and the Aurora database to theTarget account while minimizing downtime. In this solution, the Lambda function deployment package is downloaded from the Source account and used to create new Lambda functions in the Target account. The Aurora DB cluster is shared with the Target account using AWS RAM and the Target account is granted permission to clone the Aurora DB cluster, allowing for a new copy of the Aurora database to be created in the Target account. This approach allows for the data to be migrated to the Target account while minimizing downtime, as the Target account can use the cloned Aurora database while the original Aurora database continues to be used in the Source account.

This solution will give the Lambda function access to the internet by routing its outbound traffic through the NAT gateway, which has a public Elastic IP address. This will allow the external provider to whitelist the single public IP address associated with the NAT gateway, and enable the application to access the new service

Deploying a NAT gateway and associating an Elastic IP address with it, and then configuring the VPC to use the NAT gateway, will give the application the ability to access the new service. This is because the NAT gateway will be the single public IP address that the external provider needs for the allow list. The NAT gateway will allow the application to access the service, while keeping the underlying Lambda functions private.

When configuring NAT gateways, you should ensure that the route table associated with the NAT gateway has a route to the internet gateway with a target of the internet gateway. Additionally, youshould ensure that the security group associated with the NAT gateway allows outbound traffic from the Lambda functions.

CodeCommit may use S3 on the back end (and it also uses DynamoDB on the back end) but I don ' t think they ' re stored in buckets that you can see or point Macie to. In fact, there are even solutions out there describing how to copy your repo from CodeCommit into S3 to back it up:https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/automate-event-driven-backups-from-codecommit-to-amazon-s3-using-codebuild-and-cloudwatch-events.html

The correct answers are C and D. The IAM user appears to have read-only S3 permissions, but AWS authorization is the result of multiple policy layers. In AWS Organizations, a service control policy can set the maximum permissions available to principals in member accounts. If an OU-level SCP denies or does not allow S3 actions, the IAM user’s read-only policy will not be enough. A permissions boundary can also restrict the maximum permissions an IAM user can exercise, even if an identity-based policy appears to allow access. Bucket policies can affect access to specific buckets and objects, but the symptom says no buckets are listed in the console, which points more strongly to identity-side effective permission limits. ACLs are legacy resource controls and are not the primary place to troubleshoot this issue. (AWS Documentation)

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https://aws.amazon.com/getting-started/hands-on/move-to-managed/migrate-mongodb-to-documentdb/

Store the PGP Private Key:

Step 1: In the AWS Management Console, navigate to AWS Secrets Manager.

Step 2: Store the PGP private key in Secrets Manager. Ensure the key is encrypted and properly secured.

Set Up the Transfer Family Managed Workflow:

Step 1: In the AWS Transfer Family console, create a new managed workflow.

Step 2: Add a nominal step to the workflow that includes the decryption of the files. Configure this step with the PGP decryption parameters, referencing the PGP private key stored in Secrets Manager.

Step 3: Associate this workflow with the Transfer Family SFTP server, ensuring that incoming files are automatically decrypted upon receipt.

This solution ensures that the data is securely decrypted as it is transferred from the SFTP server to the S3 bucket, automating the decryption process and leveraging AWS Secrets Manager for key management.

References

AWS Transfer Family Documentation

Using AWS Secrets Manager for Managing Secrets

AWS Transfer Family Managed Workflows

The correct answer is B. This option uses IAM Identity Center to create permission sets that map to the existing IAM roles in each account. This way, the company can leverage the existing policies and roles that are already configured for the expected activities of each group of users in each account. The company also needs to create a customer managed policy that allows the group to assume the roles and attach it to thepermission set. This policy grants the necessary permissions for IAM Identity Center to assume the roles on behalf of the users. Finally, the company can assign user access to the AWS accounts in IAM Identity Center, which will automatically create IAM users and groups in each account based on the permission sets.

Option A is incorrect because it requires creating new policies in each account and giving them the same name. This is not necessary and adds complexity and overhead. The company can use the existing IAM roles and policies that are already configured for each account.

Option C is incorrect because it requires creating new policies in each account and giving them unique names. This is also not necessary and adds complexity and overhead. The company can use the existing IAM roles and policies that are already configured for each account.

Option D is incorrect because it requires adding the OU to the accounts configuration in IAM Identity Center. This is not supported by IAM Identity Center, which only allows adding individual accounts or all accounts in an organization.

This solution will meet the requirement with the least operational overhead because it directly denies the creation of the security group inbound rule with 0.0.0.0/0 as the source, which is the exact requirement. Additionally, it does not require any additional steps or resources such as invoking a Lambda function or adding a Config rule.

An SCP (Service Control Policy) is a policy that you can use to set fine-grained permissions for your AWS accounts within your organization. You can use SCPs to set permissions for the root user of an account and to delegate permissions to IAM users and roles in the accounts. You can use SCPs to set permissions that allow or deny access to specific services, actions, and resources.

To implement this solution, you would need to create an SCP that denies the ec2:AuthorizeSecurityGroupIngress action when the value of the aws:SourceIp condition key is 0.0.0.0/0. This SCP would then be applied to the NonProd OU. This would ensure that any security group inbound rule that includes 0.0.0.0/0 as the source will be denied, thus meeting the requirement.