SAA-C03 AWS Certified Solutions Architect - Associate (SAA-C03) Question and Answers

Amazon RDS Proxy is a service that provides a fully managed, highly available database proxy for Amazon RDS and Aurora databases. It allows you to pool and share database connections, reduce database load, and improve application scalability and availability.

By using Amazon RDS Proxy in front of your Aurora database, you can achieve the following benefits:

You can reduce the number of connections to your database and avoid errors that indicate that there are too many connections. Amazon RDS Proxy handles the connection management and multiplexing for you, so you can use fewer database connections and resources.

You can reduce the failover time by 20% when a read replica is promoted to primary writer. Amazon RDS Proxy automatically detects failures and routes traffic to the new primary instancewithout requiring changes to your application code or configuration. According to a benchmark test, using Amazon RDS Proxy reduced the failover time from 66 seconds to 53 seconds, which is a 20% improvement.

You can improve the security and compliance of your database access. Amazon RDS Proxy integrates with AWS Secrets Manager and AWS Identity and Access Management (IAM) to enable secure and granular authentication and authorization for your database connections.

To meet the requirements of detecting and alerting the administrators when PII is shared and automating remediation with the least development effort, the best approach would be to use Amazon S3 bucket as a secure transfer point and scan the objects in the bucket with Amazon Macie. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data stored in Amazon S3. It can be used to classify sensitive data, monitor access to sensitive data, and automate remediation actions.

In this scenario, after uploading the files to the Amazon S3 bucket, the objects can be scanned for PII by Amazon Macie, and if it detects any PII, it can trigger an Amazon Simple Notification Service (SNS) notification to alert the administrators to remove the objects containing PII. This approach requires the least development effort, as Amazon Macie already has pre-built data classification rules that can detect PII in various formats.

Hence, option B is the correct answer.

To design the API Gateway URL with the company's domain name and corresponding certificate, the company needs to do the following: 1. Create a Regional API Gateway endpoint: This will allow the company to create an endpoint that is specific to a region. 2. Associate the API Gateway endpoint with the company's domain name: This will allow the company to use its own domain name for the API Gateway URL. 3. Import the public certificate associated with the company's domain name into AWS Certificate Manager (ACM) in the same Region: This will allow the company to use HTTPS for secure communication withits APIs. 4. Attach the certificate to the API Gateway endpoint: This will allow the company to use the certificate for securing the API Gateway URL. 5. Configure Route 53 to route traffic to the API Gateway endpoint: This will allow the company to use Route 53 to route traffic to the API Gateway URL using the company's domain name.

https://aws.amazon.com/premiumsupport/knowledge-center/acm-certificate-expiration/

Amazon EFS provides a simple, scalable, fully managed file system that can be simultaneously accessed from multiple EC2 instances and provides built-in redundancy. It is optimized for multiple EC2 instances to access the same files, and it is designed to be highly available, durable, and secure. It can scale up to petabytes of data and can handle thousands of concurrent connections, and is a cost-effective solution for storing and accessing large amounts of data.

Lambda server-less is scalable and elastic than EC2 api gateway solution

Why Option D is Correct:

IAM Roles with Instance Profiles: Allow applications to access AWS services securely without hardcoding long-term access keys.

Short-Term Credentials: IAM roles issue short-term credentials dynamically managed by AWS.

Why Other Options Are Not Ideal:

Option A and B: Embedding or retrieving long-term access keys introduces security risks and operational overhead.

Option C: Combining IAM roles with Parameter Store adds unnecessary complexity.

AWS References:

IAM Roles and Instance Profiles:AWS Documentation - IAM Roles

The best practice for secure access control in AWS is to use IAM roles with least-privilege policies, granting only the permissions necessary to perform required tasks. Assigning roles individually ensures that developers cannot overstep their intended access boundaries.

Sharing credentials or using permanent access keys increases the risk of security breaches. Cognito is primarily intended for managing user access to applications, not AWS infrastructure. Thus, Option B best meets security and access control requirements.

Option Cis the most secure and practical solution. Presigned URLs allow temporary, limited access to upload files to specific S3 prefixes. This ensures that artists can upload files securely without needing AWS credentials or accounts. Each artist receives a unique URL with permissions tied to the intended S3 location, and the URL can be configured to expire after a certain time, minimizing security risks.

Why Other Options Are Incorrect:

Option A:Enabling CORS allows cross-origin access but does not provide authentication or authorization for uploads. This does not secure the uploads or restrict access to specific artists.

Option B:Turning off block public access and sharing the bucket URL exposes the bucket to potential misuse and unauthorized uploads. This approach is highly insecure.

Option D:While a web interface might work, it introduces additional complexity and potential security risks by exposing upload functionality through a public-facing application.

AWS Documentation References:

Using Amazon S3 Presigned URLs

AWS Best Practices for Secure S3 Access

Background Jobs with Event Invocation Type:

The Event invocation type is asynchronous, meaning the Lambda function does not send an immediate result to the API Gateway and processes the request in the background. This is ideal for video encoding tasks that take time.

REST API vs. HTTP API:

REST APIs support advanced features like API keys, request validation, and throttling that HTTP APIs do not support fully.

Since the developer needs API keys and request validations, a REST API is the correct choice.

Integration with Lambda:

AWS Lambda integration is seamless with REST APIs, and using the Event invocation ensures asynchronous processing.

Incorrect Options Analysis:

Option A: HTTP API lacks full support for API keys and validation.

Option CandD: RequestResponse invocation type requires immediate responses, unsuitable for background jobs.

Comprehensive and Detailed Explanation:

To handle increased loads effectively, it's essential to implement Auto Scaling for both frontend and backend tiers:

Frontend Auto Scaling Group: Scaling based on incoming network traffic ensures that the application can handle increased user requests.

Backend Auto Scaling Group: Scaling based on the Amazon SQS queue backlog ensures that the backend can process messages as they arrive, preventing delays.

This approach allows each tier to scale independently based on its specific load, ensuring optimal resource utilization and performance.

AWS Fargate with Spot capacity is the most cost-effective and serverless container option for short-duration jobs that are flexible on start time. Since the job is not latency-critical and runs nightly, it is a good candidate for Fargate Spot, which can offer up to 70% cost savings over On-Demand pricing.

The job runs for 1 hour, which exceeds the AWS Lambda maximum execution time (15 minutes). Therefore, Lambda is not suitable. EC2-based solutions involve higher operational overhead and cost, making Fargate Spot the best low-cost, low-maintenance solution.

Key Requirements:

Host the frontend on AWS as a static website.

Protect the application from common web vulnerabilities.

Minimal operational overhead.

Analysis of Options:

Option A:

Hosting the frontend on EC2 with an ALB introduces unnecessary complexity for serving static content.

AWS WAF rules can protect the ALB, but managing EC2 instances adds operational overhead.

Incorrect Approach:High operational complexity for a simple static website.

Option B:

Amazon CloudFront:Acts as a global CDN, reducing latency and protecting against DDoS attacks.

Multiple Origins:Allows static content to be served from S3 while routing API traffic to the on-premises backend.

AWS WAF:Integrates with CloudFront to provide web application protection.

Correct Approach:Offers low operational overhead with optimal security and performance.

Option C:

Using NLB and Network Firewall is unnecessary for a static website. This approach increases cost and complexity without addressing the frontend requirements effectively.

Incorrect Approach:Over-engineered solution.

Option D:

Hosting the frontend on S3 and using API Gateway is a viable option, but managing AWS WAF rules separately for both the S3 bucket and the REST API increases complexity.

Incorrect Approach:Less efficient than using CloudFront with multiple origins.

AWS Solution Architect References:

Amazon CloudFront Overview

AWS WAF with CloudFront

Amazon CloudWatch Container Insightsis designed for monitoring containerized environments like EKS. It provides native support for collecting and visualizing metrics and logs in a centralized location through CloudWatch dashboards, offering the most operationally efficient solution.

Option A:Using the CloudWatch agent provides basic metrics but lacks the specific insights required for containerized applications.

Option B:Kinesis Data Streams and Firehose add unnecessary complexity for this use case.

Option C:CloudTrail is for auditing API activity and is not designed for application metrics and log aggregation.

AWS Documentation References:

Amazon CloudWatch Container Insights

To improve read performance for a MySQL-based RDS database under load, you can:

Use Read Replicas: Amazon RDS supports MySQL read replicas, which help offload read operations from the primary database, improving performance during high traffic.

Use ElastiCache (Memcached): Adding an in-memory cache layer using Amazon ElastiCache reduces the load on the RDS instance by serving frequent queries directly from memory, especially when data is not updated often.

Option A (AWS WAF) is for web security, not database performance. Option D relates to storage optimization, not query latency. Option E would require re-architecting from relational to NoSQL, which is unnecessary and disruptive.

Amazon S3 with Amazon CloudFront:

Amazon S3 provides highly scalable and durable storage for petabytes of data.

Amazon CloudFront, as a content delivery network (CDN), caches frequently accessed data at edge locations to reduce latency. This combination is ideal for storing and accessing engineering drawings.

Incorrect Options Analysis:

Option B: Amazon S3 Glacier Deep Archive is for long-term archival storage, not frequent access.

Option C: Amazon EBS is unsuitable for large-scale, multi-user data access and does not support caching directly.

Option D: AWS Storage Gateway is for hybrid cloud storage, which is unnecessary for a fully cloud-based architecture.

Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics.

Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows.

Amazon QuickSight: Efficiently generates interactive, real-time visual reports from Timestream data.

Optimized for Scale: Timestream efficiently handles large-scale telemetry data with time-series indexing and queries.

Amazon Timestream Documentation

Option A: Importing customer-managed keys into AWS KMS ensures that encryption key material remains under the company’s control.

Option D: S3 Glacier with server-side encryption using customer-provided keys (SSE-C) complies with the need for controlled encryption and provides cost-effective storage for backups.

AWS Key Management Service Importing Keys Documentation,S3 Encryption Documentation

To securely access AWS services like S3 and Secrets Manager from a private VPC without using public IPs, interface VPC endpoints are required. These endpoints are accessible via private IP addresses. For the application to reach these endpoints, appropriate routes must be configured in the route table.

Amazon CloudFront is a highly cost-effective CDN that caches content like images and videos at edge locations globally. This reduces latency and the load on the origin S3 bucket. It is ideal for static content that is accessed by many users.

Understanding the Requirement: The company needs to mitigate DDoS attacks on its website, which uses AWS Global Accelerator to route traffic to an Application Load Balancer (ALB).

Analysis of Options:

AWS WAF on Global Accelerator: Allows for centralized protection and can block traffic based on rate-based rules, effectively mitigating DDoS attacks with minimal implementation effort.

Lambda Function and VPC Network ACL: Requires custom implementation and ongoing management, increasing complexity and effort.

AWS WAF on ALB: Provides protection but involves additional configuration and management at the ALB level.

CloudFront Distribution in front of Global Accelerator: Adds unnecessary complexity and changes the current traffic flow setup.

Best Solution:

AWS WAF on Global Accelerator: This provides the required protection with the least implementation effort, ensuring effective DDoS mitigation and maintaining the existing architecture.

DynamoDB Streams: Captures real-time changes in DynamoDB tables and allows integration with Lambda for processing the changes.

Minimal Operational Overhead: Using a Lambda function directly to write data to S3 ensures simplicity and reduces the complexity of the pipeline.

Amazon DynamoDB Streams Documentation

AWS App2Container is a command-line tool that helps containerize existing Java and .NET applications running on-premises or on virtual machines, with minimal refactoring. By using Amazon EKS, the company benefits from a managed Kubernetes service, which significantly reduces the operational overhead compared to managing Kubernetes on EC2.

Amazon RDS for SQL Server provides a fully managed SQL Server database engine with automated backups, patching, and high availability through Multi-AZ deployments. This eliminates the need for the company to manage database infrastructure and software manually.

Overall, option A provides the most streamlined and managed approach for both the application and database layers with the least operational effort.

This option is the most efficient because it uses Amazon SQS, which is a fully managed message queuing service that lets you send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available1. It also uses an SQS message queue to asynchronously dispatch requests to the different application tiers, which decouples the image upload process from the thumbnail generation process and enables scalability and reliability. It also alerts the user through an application message that the image was received, which provides a faster response time to the user than waiting for the thumbnail generation to complete. Option A is less efficient because it uses a custom AWS Lambda function to generate the thumbnail and alert the user, which is a way to run code without provisioning or managing servers. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses the image upload process as an event source to invoke the Lambda function, which could cause concurrency issues if there are many images uploaded at once. Option B is less efficient because it uses AWS Step Functions, whichis a fully managed service that provides a graphical console to arrange and visualize the components of your application as a series of steps2. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses Step Functions to handle the orchestration between the application tiers and alert the user when thumbnail generation is complete, which could introduce additional complexity and latency. Option D is less efficient because it uses Amazon SNS, which is a fully managed messaging service that enables you to send messages or notifications directly to users with SMS text messages or email3. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses SNS notification topics and subscriptions to generate the thumbnail after the image upload is complete and message the user’s mobile app by way of a push notification after thumbnail generation is complete, which could introduce additional complexity and latency.

Option B: Pre-signed URLs provide temporary, authenticated access to S3, limiting uploads to the time frame specified. This solution is lightweight, efficient, and easy to implement.

Option Arequires the management of IAM temporary credentials, adding complexity.

Option Cinvolves unnecessary development effort.

Option Dintroduces more complexity with STS and roles than pre-signed URLs.

AWSService Catalogis designed to allow organizations to manage a catalog of approved products (including AMIs) that users can deploy. By creating a portfolio that contains only EC2 instances launched with preapproved AMIs, the company can enforce compliance with the approved operating system and software for all EC2 instances. Service Catalog also streamlines the process of launching EC2 instances, reducing the lead time while ensuring that developers use only the approved configurations.

Option B (EC2 Image Builder): While EC2 Image Builder helps in creating and managing AMIs, it doesn't provide the enforcement mechanism that Service Catalog does.

Option C (EventBridge rule and Systems Manager script): This solution is reactive and involves more operational complexity compared to Service Catalog.

Option D (AWS Config rule): This option is reactive (it terminates non-compliant instances after launch) and introduces additional operational overhead.

AWS References:

AWS Service Catalog

To achieve high availability for the website, two key actions should be taken:

Amazon RDS for MySQL Multi-AZ: By migrating the database to an RDS for MySQL Multi-AZ deployment, the database becomes highly available. Multi-AZ provides automatic failover from the primary database to a standby replica in another Availability Zone, ensuring database availability even in the case of an AZ failure.

Application Load Balancer and Auto Scaling: Deploying an Application Load Balancer (ALB) in front of the EC2 instances ensures that traffic is evenly distributed across the instances. Configuring an Auto Scaling group to run EC2 instances across multiple Availability Zones ensures that the application remains available even if one instance or one AZ becomes unavailable. This setup enhances fault tolerance and improves reliability.

Why Not Other Options?:

Option A (Internet Gateway per AZ): Internet Gateways are region-wide resources and do not need to be provisioned per Availability Zone. This option does not contribute to high availability.

Option C (DynamoDB + Auto Scaling): DynamoDB would require changes to the application code to switch from MySQL, which is not possible per the question's constraints.

Option D (DataSync): AWS DataSync is used for data transfer and synchronization, not for achieving high availability for a database.

AWS References:

Amazon RDS Multi-AZ Deployments- Explanation of how Multi-AZ deployments work in Amazon RDS.

Application Load Balancing- Details on how to configure and use ALB for distributing traffic across multiple instances.

Amazon RDS Proxy: RDS Proxy is a fully managed, highly available database proxy that makes applications more resilient to database failures by pooling and sharing connections, and it can automatically handle database failovers.

Reduced Failover Time: By using RDS Proxy, the connection management between the application and the database is improved, reducing failover times significantly. RDS Proxy maintains connections in a connection pool and reduces the time required to re-establish connections during a failover.

Configuration:

Create an RDS Proxy instance.

Configure the proxy to connect to the RDS for PostgreSQL DB instance.

Modify the application configuration to use the RDS Proxy endpoint instead of the direct database endpoint.

Operational Benefits: This solution provides high availability and reduces application timeouts during failovers with minimal changes to the application code.

Using S3 event notifications to trigger AWS Lambda for file processing is a cost-effective and serverless solution. Lambda scales automatically with upload volume, and processing each file takes less than 5 seconds, fitting within Lambda's execution time.

Option A:AWS AppSync is designed for GraphQL APIs and is not suitable for file processing.

Option C:Kinesis is overkill and more expensive for this use case.

Option D:Running an EC2 instance incurs ongoing costs and is less flexible compared to Lambda.

AWS Documentation References:

Amazon S3 Event Notifications

AWS Lambda Overview

AWS Glue jobs are designed for extract, transform, and load (ETL) operations, which are perfect for transforming clinical trial data. AWS Glue integrates with AWS Key Management Service (KMS), allowing for customer-specific encryption keys, fulfilling the encryption requirement with minimal operational effort. Client-side encryption with AWS KMS ensures that the data is encrypted before it is sent to S3, aligning with the security needs specified in the scenario.

Key aspects:

AWS Glue: This managed ETL service simplifies data transformation, reduces operational overhead, and integrates seamlessly with KMS.

CSE-KMS: Client-side encryption with KMS ensures that the data is encrypted with customer-specific keys before it is processed or stored in S3, offering robust security​​.

Minimal Operational Overhead: Compared to managing an EMR cluster, AWS Glue automates much of the process, making it a lower-effort solution.

AWS Documentation: According to the AWS Well-Architected Framework, encryption with AWS KMS offers strong security controls that meet the needs of industries requiring high levels of confidentiality​​.

Using S3 gateway endpoints allows private and cost-free access to S3 without routing traffic through a NAT gateway. NAT gateway traffic incurs charges, especially when used across multiple Availability Zones.

By using an S3 gateway endpoint, EC2 instances in private subnets can access S3 directly without needing internet access, reducing both data transfer and NAT gateway costs.

Interface endpoints are more expensive and typically used for services like API Gateway or Systems Manager.

Big data workloads that need to process terabytes of data in memory requirememory-optimized instancesfor the core and task nodes to ensure sufficient memory for processing data efficiently.

Primary Node:Ageneral purpose instanceis suitable because it manages cluster operations, including coordination and monitoring, and does not process data directly.

Core and Task Nodes:These nodes handle data storage and processing.Memory-optimized instancesare ideal because they provide high memory-to-CPU ratios, which is critical for in-memory big data workloads.

Why Other Options Are Incorrect:

Option A:Storage optimized and compute optimized instances are not suitable for workloads that rely heavily on in-memory processing.

Option B:A memory-optimized primary node is unnecessary because the primary node does not process data.

Option D:General purpose instances for all nodes will not provide sufficient memory for processing terabytes of data in memory.

AWS Documentation References:

Amazon EMR Instance Types

Memory-Optimized Instances

To handle unpredictable traffic surges and improve scalability, Auto Scaling is essential. Creating an AMI and deploying it into an Auto Scaling group behind an Application Load Balancer (ALB) allows AWS to automatically scale the number of EC2 instances based on demand.

This architecture improves availability and responsiveness by distributing traffic and launching instances when needed. Option A satisfies the need for high scalability and resilience. Other options either lack Auto Scaling or are more complex without added benefit in this context.

(https://aws.amazon.com/blogs/security/how-to-restrict-amazon-s3-bucket-access-to-a-specific-iam-role/)

https://aws.amazon.com/ec2/dedicated-hosts/ Amazon EC2 Dedicated Hosts allow you to use your eligible software licenses from vendors such as Microsoft and Oracle on Amazon EC2, so that you get the flexibility and cost effectiveness of using your own licenses, but with the resiliency, simplicity and elasticity of AWS.

To allocate costs based on team ownership, AWS recommends tagging resources using cost allocation tags. Activating the “owner” tag as a cost allocation tag allows AWS Cost Explorer to categorize and group spending. Additionally, filtering for Amazon ECS services enables the visibility into service-specific usage. This approach is both scalable and operationally efficient.

EC2 Auto Scaling warm pools allow you to pre-initialize instances, reducing the delay in scale-out events. This results in significantly faster response times during demand surges while remaining cost-effective compared to always running at peak capacity.

Concurrency Scaling allows Amazon Redshift to add transient capacity automatically to handle bursts in concurrent queries. This is ideal for scenarios with predictable surge periods, such as end-of-month reporting.

It improves performance without manual resizing or cluster modification and without affecting availability. Resizing requires manual intervention and potential downtime, and Redshift Spectrum is designed for querying data in S3, not for solving concurrency issues.

"In some cases, this connection alone is not enough. It is always better to guarantee a fallback connection as the backup of DX. There are several options, but implementing it with an AWS Site-To-Site VPN is a real cost-effective solution that can be exploited to reduce costs or, in the meantime, wait for the setup of a second DX."

https://www.proud2becloud.com/hybrid-cloud-networking-backup-aws-direct-connect-network-connection-with-aws-site-to-site-vpn/

"Create an Amazon SQS queue to hold the jobs that needs to be processed. Create an Amazon EC2 Auto Scaling group for the compute application. Set the scaling policy for the Auto Scaling group to add and remove nodes based on the number of items in the SQS queue"

In this case we need to find a durable and loosely coupled solution for storing jobs. Amazon SQS is ideal for this use case and can be configured to use dynamic scalingbased on the number of jobs waiting in the queue.To configure this scaling you can use the backlog per instance metric with the target value being the acceptable backlog per instance to maintain. You can calculate these numbers as follows: Backlog per instance: To calculate your backlog per instance, start with the ApproximateNumberOfMessages queue attribute to determine the length of the SQS queue

AWS Step Functions supports long-running workflows and error retries, making it ideal for a job composed of many tasks. Integration with EventBridge allows automatic triggering from S3 events. This setup is resilient and supports up to 1-year execution duration.

The most suitable solution for the company’s requirements is to configure AWS Glue DataBrew to transform the data and share the transformation steps with employees by using DataBrew recipes. This solution will provide a prebuilt solution for data transformation that does not require code, and will also provide data lineage and data profiling. The company can easily share the data transformation steps with employees throughout the company by using DataBrew recipes.

AWS Glue DataBrew is a visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data for analytics or machine learning by up to 80% faster. Users can upload their data from various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, Amazon Aurora, or Glue Data Catalog, and use a point-and-click interface to apply over 250 built-in transformations. Users can also preview the results of each transformation step and see how it affects the quality and distribution of the data1.

A DataBrew recipe is a reusable set of transformation steps that can be applied to one or more datasets. Users can create recipes from scratch or use existing ones from the DataBrew recipe library. Users can also export, import, or share recipes with other users or groups within their AWS account or organization2.

DataBrew also provides data lineage and data profiling features that help users understand and improve their data quality. Data lineage shows the source and destination of each dataset and how it is transformed by each recipe step. Data profiling shows various statistics and metrics about each dataset, such as column

This solution will meet the requirements because Amazon FSx for NetApp ONTAP is a fully managed service that provides highly reliable, scalable, and feature-rich file storage built on NetApp’s popular ONTAP file system. FSx for ONTAP supports both NFS and SMB protocols, which means it can be accessed by both Linux and Windows applications without code changes. FSx for ONTAP also eliminates data duplication and inefficient resource usage by automatically tiering infrequently accessed data to a lower-cost storage tier and providing storage efficiency features such as deduplication and compression. FSx for ONTAP also integrates with other AWS services such as Amazon S3, AWS Backup, and AWS CloudFormation. By migrating the applications to Amazon EC2 instances, the company can leverage the scalability, security, and performance of AWS compute resources. 

These options are the best solutions because they allow the company to run the application with Docker containers in the AWS Cloud using a managed service that scales automatically and does not require any infrastructure to manage. By using AWS Fargate, the company can launch and run containers without having to provision, configure, or scale clusters of EC2 instances. Fargate allocates the right amount of compute resources for each container and scales them up or down as needed. By using Amazon ECS or Amazon EKS, the company can choose the container orchestration platform that suits its needs. Amazon ECS is a fully managed service that integrates with other AWS services and simplifies the deployment and management of containers. Amazon EKS is a managed service that runs Kubernetes on AWS and provides compatibility with existing Kubernetes tools and plugins.

C. Provision an Amazon API Gateway API Connect the API to AWS Lambda to run the containers. This option is not feasible because AWS Lambda does not support running Docker containers directly. Lambda functions are executed in a sandboxed environment that is isolated from other functions and resources. To run Docker containers on Lambda, the company would need to use a custom runtime or a wrapper library that emulates the Docker API, which can introduce additional complexity and overhead.

D. Use Amazon Elastic Container Service (Amazon ECS) with Amazon EC2 worker nodes. This option is not optimal because it requires the company to manage the EC2 instances that host the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs.

E. Use Amazon Elastic Kubernetes Service (Amazon EKS) with Amazon EC2 worker nodes. This option is not ideal because it requires the company to manage the EC2 instances that host the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs.

AWS Storage Gateway is a hybrid cloud storage service that allows you to seamlessly integrate your on-premises applications with AWS cloud storage. Volume Gateway is a type of Storage Gateway that presents cloud-backed iSCSI block storage volumes to your on-premises applications. Volume Gateway operates in either cache mode or stored mode. In cache mode, your primary data is stored in Amazon S3, while retaining your frequently accessed data locally in the cache for low latency access. In stored mode, your primary data is stored locally and your entire dataset is available for low latency access on premises while also asynchronously getting backed up to Amazon S3.

For the pharmaceutical company’s use case, cache mode is the most suitable option, as it meets the following requirements:

It reduces the need to scale the on-premises storage infrastructure, as most of the data is stored in Amazon S3, which is scalable, durable, and cost-effective.

It provides low latency access to the subset of the data that the researchers regularly require, as it is cached locally in the Storage Gateway appliance.

It does not require the entire dataset to be accessed on a daily basis, as it is stored in Amazon S3 and can be retrieved on demand.

It offers flexible data protection and recovery options, as it allows taking point-in-time copies of the volumes using AWS Backup, which are stored in AWS as Amazon EBS snapshots.

Therefore, the solutions architect should recommend deploying an AWS Storage Gateway volume gateway with cached volumes with an Amazon S3 bucket as the target storage and migrating the data to the Storage Gateway appliance.

AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. You can create Lambda functions using various languages, including Java, and specify the amount of memory and CPU allocated to your function. Lambda charges you only for the compute time you consume, which is calculated based on the number of requests and the duration of your code execution. You can use Amazon EventBridge to trigger your Lambda function on a schedule, such as every hour, using cron or rate expressions. This solution will optimize the costs to run the job, as you will not payfor any idle time or unused resources, unlike running the job on an EC2 instance. References: 1: AWS Lambda - FAQs2, General Information section2: Tutorial:Schedule AWS Lambda functions using EventBridge3, Introduction section3: Schedule expressions using rate or cron - AWS Lambda4, Introduction section.

AWS Snowball is a petabyte-scale data transport service that uses secure devices to transfer large amounts of data into and out of the AWS Cloud. Snowball addresses common challenges with large-scale data transfers including high network costs, long transfer times, and security concerns. AWS Snowball can transfer up to 80 TB of data per device, and multiple devices can be used in parallel to meet the migration deadline. AWS Snowball is more cost-effective than AWS Snowmobile, which is designed for exabyte-scale data transfers, or Amazon S3 Transfer Acceleration, which is optimized for fast transfers over long distances. Amazon S3 VPC endpoint does not increase the upload speed, but only provides a secure and private connection between the VPC and S3. References: AWS Snowball, AWS Snowmobile, Amazon S3 Transfer Acceleration, Amazon S3 VPC endpoint

TheAWS WAF Bot Control managed ruleis designed to automatically detect and mitigate bot traffic. This feature is particularly useful for addressing malicious traffic and conserving compute resources by filtering unnecessary requests at the ALB level.

Option A:Blocking IP addresses manually introduces significant operational overhead and is not scalable against dynamic, worldwide malicious traffic.

Option C:AWS Shield provides DDoS protection, but the scenario does not describe a DDoS attack. WAF is better suited for managing application-layer threats like bot traffic.

Option D:The AWS WAF IP reputation rule helps block traffic from known bad IPs but may not address bot traffic effectively.

AWS Documentation References:

AWS WAF Bot Control

AWS WAF Managed Rules

AWS Config allows for creation of custom rules to monitor EBS configurations. Combined with CloudWatch metrics and Amazon SNS, custom rules can track over-provisioned IOPS and send alerts when thresholds are breached, allowing proactive cost and performance management.

AWS Secrets Manager is the recommended service for managing and automatically rotating database credentials. It integrates natively with Amazon RDS (including PostgreSQL), supports built-in rotation functionality, and requires minimal setup.

Secrets Manager also supports versioning and auditing, which enhances operational excellence and security. Parameter Store does not natively support credential rotation. AWS KMS manages key encryption—not application secrets—so it is not applicable here.

Why Option D is Correct:

GPU Access: Only EC2 instances in the GPU family (e.g., P2, P3) can provide GPU resources.

ECS Orchestration: Simplifies container deployment and management.

Why Other Options Are Not Ideal:

Option A: Lambda does not support GPU-based runtimes.

Option B: AWS Fargate does not support GPU-based workloads.

Option C: ECR is a container registry, not an orchestration or execution service.

AWS References:

Amazon ECS with GPU Instances:AWS Documentation - ECS GPU Instances

Amazon RDS for MySQL is a fully-managed relational database service that makes it easy to set up, operate, and scale MySQL deployments in the cloud. Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora (MySQL-compatible edition), where the database will automatically start up, shut down, and scale capacity up or down based on your application’s needs. It is a simple, cost-effective option for infrequent, intermittent, or unpredictable workloads.

Partition Key Issue:

Using "service name" as the partition key results in uneven data distribution. Some shards may become hot due to excessive logs from certain services, leading to throttling errors.

Changing the partition key to "creation timestamp" ensures a more even distribution of records across shards.

Incorrect Options Analysis:

Option A: On-demand capacity mode eliminates throughput management but is more expensive and does not address the root cause.

Option B: Adding more shards does not solve the issue if the partition key still creates hot shards.

Option D: Using separate streams increases complexity and is unnecessary.

To design a resilient solution that can improve the recovery time for the system, a solutions architect should recommend creating an Amazon CloudWatch alarm to recover the EC2 instance in case of failure. This solution has the following benefits:

It allows the EC2 instance to be automatically recovered when a system status check failure occurs, such as loss of network connectivity, loss of system power, software issues on the physical host, or hardware issues on the physical host that impact network reachability1.

It preserves the instance ID, private IP addresses, Elastic IP addresses, and all instance metadata of the original instance. A recovered instance is identical to the original instance, except for any data that is in-memory, which is lost during the recovery process1.

It does not require any modification of the application code or the EC2 instance configuration. The solutions architect can create a CloudWatch alarm using the AWS Management Console, the AWS CLI, or the CloudWatch API2.

AWS Database Migration Service supports homogeneous migrations such as Oracle to Oracle, as well as heterogeneous migrations between different database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora. With AWS Database Migration Service, you can also continuously replicate data with low latency from any supported source to any supported target. For example, you can replicate from multiple sources to Amazon Simple Storage Service (Amazon S3) to build a highly available and scalable data lake solution. You can also consolidate databases into a petabyte-scale data warehouse by streaming data to Amazon Redshift. Learn more about the supported source and target databases.https://aws.amazon.com/dms/

Comprehensive and Detailed Explanation:

To grant cross-account access to an Amazon S3 bucket, you can use a bucket policy that specifies the AWS account ID of the account you want to grant access to. This method allows Account B to access the S3 bucket in Account A without the need for additional services or configurations.

https://aws.amazon.com/step-functions/#:~:text=AWS%20Step%20Functions%20is%20a,machine%20learning%20(ML)%20pipelines.

"A common use case for AWS Step Functions is a task that requires human intervention (for example, an approval process). Step Functions makes it easy to coordinate the componentsof distributed applications as a series of steps in a visual workflow called a state machine. You can quickly build and run state machines to execute the steps of your application in a reliable and scalable fashion. (https://aws.amazon.com/pt/blogs/compute/implementing-serverless-manual-approval-steps-in-aws-step-functions-and-amazon-api-gateway/) "

Understanding the Requirement: The application running on EC2 instances in a private subnet needs to process sensitive information from an S3 bucket without using the internet.

Analysis of Options:

Internet Gateway: This would expose the application to the internet, which is not suitable for accessing sensitive information securely.

VPN Connection: VPN is primarily used for secure connections between on-premises networks and AWS VPCs, not for direct S3 access within the same VPC.

NAT Gateway: This allows instances in a private subnet to connect to the internet, but the goal is to avoid internet access.

VPC Endpoint: Provides a private connection between the VPC and S3 without using the internet, ensuring secure access to the S3 bucket.

Best Solution:

VPC Endpoint: Configuring a VPC endpoint allows secure, private communication between the EC2 instances and the S3 bucket without using the internet, ensuring data security and compliance.

A service control policy (SCP) is a type of policy that you can use to manage permissions in your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing you to ensure your accounts stay within your organization’s access control guidelines. SCPs are available only in an organization that has all features enabled. SCPs do not grant permissions; instead, they specify the maximum permissions for an organization or organizational unit (OU). SCPs limit permissions that identity-basedpolicies or resource-based policies grant to entities (users or roles) within the account, but do not grant permissions to entities. You can use SCPs to restrict the actions that the root user in an account can perform. You can also use SCPs to prevent users or roles in any account from creating or modifying certain AWS resources, such as EC2 instances with public IP addresses or IAM resources with inline policies or “". For example, you can create an SCP that denies the ec2:RunInstances action if the request includes the AssociatePublicIpAddress parameter set to true. You can also create an SCP that denies the iam:PutUserPolicy and iam:PutRolePolicy actions if the request includes a policy document that contains "”. By attaching these SCPs to your organization or OUs, you can prevent the deployment of AWS CloudFormation stacks that violate these rules.

AWS Control Tower proactive controls are guardrails that enforce preventive policies on your accounts and resources. Proactive guardrails are implemented as AWS Organizations service control policies (SCPs) and AWS Config rules. However, AWS Control Tower does not provide a built-in proactive guardrail to block EC2 instances with public IP addresses or IAM resources with inline policies or “*”. You would have to create your own custom guardrails using AWS CloudFormation templates and SCPs, which is essentially the same as option D. Therefore, option A is not correct.

AWS Control Tower detective controls are guardrails that detect and alert on policy violations in your accounts and resources. Detective guardrails are implemented as AWS Config rules and Amazon CloudWatch alarms. Detective guardrails do not block or remediate noncompliant resources; they only notify you of the issues. Therefore, option B is not correct.

AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. AWS Config continuously monitors and records your AWS resource configurations and allows you to automate the evaluation of recorded configurations against desired configurations. AWS Config rules are customizable, AWS Lambda functions that AWS Config invokes to evaluate your AWS resource configurations. You can use AWS Config rules to check for compliance with your policies, such as ensuring that EC2 instances have public IP addresses disabled or IAM resources do not have inline policies or “*”. However, AWS Config rules alone cannot prevent the deployment of AWS CloudFormation stacks that violate these policies; they can only report the compliance status. You would need to use another service, such as AWS Systems Manager Session Manager, to run automation scripts to delete or modify the noncompliant resources. This would require additional configuration and permissions, and may not be the most efficient or secure way to enforce your policies. Therefore, option C is not correct.

Requirement Analysis: The application was overwhelmed with unexpected data volume, leading to data loss and the need for a replay mechanism.

Amazon SQS Overview: SQS is a fully managed message queuing service that decouples and scales microservices, distributed systems, and serverless applications.

Data Decoupling: By using an SQS queue, the application can store incoming tracker data reliably and process it asynchronously, preventing data loss.

Implementation:

Create an SQS queue.

Modify the web application to send incoming data to the SQS queue.

Configure the application instances to poll the SQS queue and process the messages.

Conclusion: This solution meets the requirements with minimal operational overhead, ensuring data is not lost and can be processed at the application’s own pace.

References

Amazon SQS:Amazon SQS Documentation

Understanding the Requirement: The company needs to design a scalable and serverless solution for a near-real-time streaming application that experiences high latency due to large amounts of incoming data. The job processing takes about 30 minutes.

Analysis of Options:

Amazon Kinesis Data Firehose: Provides a fully managed service for real-time data streaming and ingestion, allowing for seamless data delivery to destinations such as Amazon S3, Redshift, and Elasticsearch.

AWS Lambda with AWS Step Functions: While suitable for orchestration and lightweight processing, Lambda might not handle long-running jobs (max 15 minutes execution limit) efficiently.

AWS DMS: Primarily used for database migration, not for real-time data ingestion in this context.

Amazon EC2 in Auto Scaling Group: Provides scalability but involves managing servers, which is not serverless and adds operational overhead.

AWS Fargate with ECS: Offers a serverless compute engine for containers, allowing easy scaling and management without managing the underlying infrastructure.

Best Solution:

Amazon Kinesis Data Firehose: For ingesting the streaming data efficiently.

AWS Fargate with ECS: For processing the data in a scalable and serverless manner.

Understanding the Requirement: Senior leadership wants a custom dashboard displaying NAT gateway costs daily, starting from the beginning of the current month.

Analysis of Options:

QuickSight with DataSync: While QuickSight is suitable for dashboards, DataSync is not designed for querying and analyzing data reports.

QuickSight with Athena: QuickSight can visualize data queried by Athena, which is designed to analyze data directly from S3.

CloudWatch with DataSync: CloudWatch is primarily for monitoring metrics, not for creating detailed cost analysis dashboards.

CloudWatch with Athena: Similarly, using CloudWatch with Athena does not align well with the requirement for a visual dashboard.

Best Solution for Visualization and Querying:

Amazon QuickSight with Athena: This combination allows for powerful data visualization and querying capabilities. QuickSight can create dynamic dashboards, while Athena efficiently queries the cost and usage report data stored in S3.

Understanding the Requirement: The application is stateful and requires at least two EC2 instances to be running at all times, with a highly available and fault-tolerant architecture.

Analysis of Options:

Minimum capacity of two with instances in separate AZs: Ensures high availability by distributing instances across multiple AZs, fulfilling the requirement of always having two instances running.

Minimum capacity of four: Provides redundancy but is more than what is required and increases cost without additional benefit.

Spot Instances: Not suitable for a stateful application requiring guaranteed availability, as Spot Instances can be terminated at any time.

Combination of On-Demand and Spot Instances: Mixing instance types might provide cost savings but does not ensure the required availability for a stateful application.

Best Solution:

Minimum capacity of two with instances in separate AZs: This setup ensures high availability and meets the requirement with the least cost and complexity.

Understanding the Requirement: The university wants to centralize management and automate backups for its AWS services (EC2, RDS, and DynamoDB), reducing reliance on custom scripts.

Analysis of Options:

Third-party backup software with AWS Storage Gateway: This solution introduces external dependencies and adds complexity compared to using native AWS services.

AWS Backup: Provides a centralized, fully managed service to automate and manage backups across various AWS services, including EC2, RDS, and DynamoDB.

AWS Config: Primarily used for compliance and configuration monitoring, not for backup management.

AWS Systems Manager State Manager: Useful for configuration management but not specifically designed for managing backups.

Best Solution:

AWS Backup: This service offers the necessary functionality to centralize and automate backups, providing a streamlined and integrated solution with minimal effort.

Requirement Analysis: The application needs near real-time access to data, persistence, and minimal operational overhead.

ElastiCache for Redis: Provides in-memory data storage with persistence, supporting fast access and durability.

Operational Overhead: Managed service reduces the burden of setup, maintenance, and scaling.

Implementation:

Deploy an ElastiCache for Redis cluster.

Configure Redis to persist data to disk using AOF (Append-Only File) or RDB (Redis Database Backup) snapshots.

Conclusion: ElastiCache for Redis meets the requirements for fast access, data persistence, and low operational overhead.

References

Amazon ElastiCache:ElastiCache for Redis Documentation

Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.

Analysis of Options:

Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.

IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.

IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.

IAM Group in Production Account: This approach does not provide the required cross-account access control.

Best Solution:

IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.

Understanding the Requirement: EC2 instances need to upload data to S3 without using the public internet, and on-premises servers need to consume this data.

Analysis of Options:

Interface VPC Endpoint for EC2: Not relevant for accessing S3.

Gateway VPC Endpoint for S3 and Direct Connect: Provides private connectivity from EC2 instances to S3 and from on-premises to AWS, ensuring compliance with the requirement to avoid public internet.

Transit Gateway and Site-to-Site VPN: Adds unnecessary complexity and does not provide the same level of performance as Direct Connect.

Proxy EC2 Instances with NAT Gateways: Increases complexity and costs compared to a direct connection using VPC endpoints and Direct Connect.

Best Solution:

Gateway VPC Endpoint for S3 and Direct Connect: This solution ensures secure, private data transfer both within AWS and between on-premises and AWS, meeting the compliance requirements effectively.

Understanding the Requirement: The company needs to automate inventory and updates of diverse OS versions on EC2 instances and summarize common vulnerabilities for monthly reviews.

Analysis of Options:

Systems Manager Patch Manager and Security Hub: Patch Manager automates patching, but Security Hub is more focused on compliance and security posture rather than inventory and vulnerability management.

Systems Manager Patch Manager and Amazon Inspector: Patch Manager automates OS updates, and Amazon Inspector provides vulnerability assessments, making this a comprehensive solution for the requirements.

AWS Shield Advanced and AWS Config: Shield Advanced is for DDoS protection, not suitable for OS patch management and vulnerability reporting.

Amazon GuardDuty and AWS Config: GuardDuty is for threat detection and monitoring, not specifically for patch management and vulnerability assessments.

Best Solution:

Systems Manager Patch Manager and Amazon Inspector: This combination automates OS updates and provides detailed vulnerability assessments, meeting both the inventory and security reporting needs effectively.

Understanding the Requirement: The company needs to copy files generated by an on-premises application to AWS without modifying the application code. The files are stored on an SMB file share and require a low-latency connection to the application servers.

Analysis of Options:

Amazon Elastic File System (EFS): EFS is designed for Linux-based workloads and does not natively support SMB file shares.

Amazon FSx for Windows File Server: FSx supports SMB file shares but would require changes to the application or additional infrastructure to connect on-premises systems.

AWS Snowball: Suitable for large data transfers but not for continuous, low-latency file copying.

AWS Storage Gateway: Provides a hybrid cloud storage solution, supporting SMB file shares and enabling seamless copying of files to AWS without requiring changes to the application.

Best Solution:

AWS Storage Gateway: This service meets the requirement for a low-latency, seamless file transfer solution from on-premises to AWS without modifying the application code.

Understanding the Requirement: The company needs to control the creation, rotation, and disabling of encryption keys for data stored in S3 with minimal effort.

Analysis of Options:

SSE-S3: Provides server-side encryption using S3 managed keys but does not offer full control over key management.

Customer managed key with AWS KMS (SSE-KMS): Allows the company to fully control key creation, rotation, and disabling, providing a high level of security and compliance.

AWS managed key with AWS KMS (SSE-KMS): While it provides some control, it does not offer the same level of granularity as customer-managed keys.

EC2 instance encryption and re-upload: This approach is operationally intensive and does not leverage AWS managed services for efficient key management.

Best Solution:

Customer managed key with AWS KMS (SSE-KMS): This solution meets the requirement for full control over encryption keys with minimal operational overhead, leveraging AWS managed services for secure key management.

Understanding the Requirement: The order processing system requires multiple independent validation steps, each handled by separate Lambda functions, with each function accessing only the subset of order information it needs. The system should be loosely coupled to accommodate future changes.

Analysis of Options:

Amazon SQS with a new Lambda function for transformation: This involves additional complexity in creating and managing multiple SQS queues and an extra Lambda function for data transformation.

Amazon SNS with message filtering: While SNS supports message filtering, it is more suited for pub/sub messaging patterns rather than event-driven processing requiring fine-grained control over the data sent to each function.

Amazon EventBridge with input transformers: EventBridge is designed for event-driven architectures, allowing for fine-grained control with input transformers that can modify and filter the event data sent to each target Lambda function, ensuring each function receives only the necessary information.

SQS with synchronous Lambda invocations: This approach adds unnecessary complexity with synchronous invocations and is not ideal for an event-driven, loosely coupled architecture.

Best Solution:

Amazon EventBridge with input transformers: This option provides the most flexible, scalable, and loosely coupled architecture, enabling each Lambda function to receive only the required subset of data.

Since the application has no local data on instances, AMIs alone can meet the RPO by restoring instances from the most recent AMI backup. When combined with automated RDS backups for the database, this provides a complete backup solution for this environment. The other options involving EBS snapshots would be unnecessary given the stateless nature of the instances. AMIs provide all the backup needed for the app tier. This uses native, automated AWS backup features that require minimal ongoing management: - AMI automated backups provide point-in-time recovery for the stateless app tier. - RDS automated backups provide point-in-time recovery for the database.

Requirement Analysis: The product catalog search is causing high CPU utilization on the MySQL DB instance, slowing down the website.

ElastiCache Overview: Amazon ElastiCache for Redis can be used to cache frequently accessed data, reducing load on the database.

Lazy Loading: This caching strategy loads data into the cache only when it is requested, improving response times for repeated queries.

Implementation:

Set up an ElastiCache for Redis cluster.

Modify the application to check the cache before querying the database.

Use lazy loading to populate the cache on cache misses.

Conclusion: This approach reduces database load and improves website performance during product catalog searches.

References

Amazon ElastiCache:ElastiCache Documentation

Caching Strategies:ElastiCache Caching Strategies

Requirement Analysis: The Lambda function in the administrator account needs to process messages from an SNS topic in the production account.

IAM Policy for SNS Topic: Allows the Lambda function to subscribe and be invoked by the SNS topic.

SQS Queue for Buffering: Using an SQS queue provides reliable message delivery and buffering between SNS and Lambda, ensuring all messages are processed.

Implementation:

Create an SQS queue in the administrator account.

Set an IAM policy to allow the Lambda function to subscribe to and be invoked by the SNS topic.

Configure the SNS topic to send messages to the SQS queue.

Set up the SQS queue to trigger the Lambda function.

Conclusion: This solution ensures reliable message delivery and processing with appropriate permissions.

References

Amazon SNS:Amazon SNS Documentation

Amazon SQS:Amazon SQS Documentation

AWS Lambda:AWS Lambda Documentation

Understanding the Requirement: The company needs to store a copy of all new photos in the us-east-1 Region from an S3 bucket in the us-west-1 Region.

Analysis of Options:

Cross-Region Replication: Automatically replicates objects across regions with minimal operational effort once configured.

CORS Configuration: Used for allowing resources on a web page to be requested from another domain, not for replication.

S3 Lifecycle Rule: Manages the transition of objects between storage classes within the same bucket, not for cross-region replication.

S3 Event Notifications with Lambda: Requires additional configuration and management compared to Cross-Region Replication.

Best Solution:

S3 Cross-Region Replication: This solution provides an automated and efficient way to replicate objects to another region, meeting the requirement with the least operational effort.

Understanding the Requirement: The company wants to centrally collect security data with minimal development effort to assess and improve security across all workloads.

Analysis of Options:

Amazon Security Lake: This is a purpose-built service for centralizing security data from across AWS services and third-party sources into a data lake. It provides native integration and requires minimal development effort to set up.

AWS Lake Formation with AWS Glue: While this can be used to create a data lake, it requires more development effort to set up and configure Glue crawlers for ingestion.

AWS Lambda with S3: This approach involves custom development to collect and process security data before storing it in S3, which requires more effort.

AWS DMS to RDS: AWS Database Migration Service is typically used for database migrations and is not suited for collecting and analyzing security data.

Best Option for Minimal Development Effort:

Amazon Security Lake provides the least development effort for setting up a centralized repository for security data. It simplifies data ingestion and management, making it the most efficient solution for this use case.

Understanding the Requirement: The company needs to transfer 50 TB of data to AWS with minimal operational overhead and no available network bandwidth for the transfer. The transformation job must continue running in the AWS Cloud.

Analysis of Options:

AWS DataSync and AWS Glue: DataSync is suitable for online data transfer, but there is no available network bandwidth. AWS Glue can be used for data transformation but does not solve the bandwidth issue.

AWS Snowcone: Snowcone is a smaller device suitable for smaller data transfers, and deploying the transformation application on it may not be feasible for 50 TB of data.

AWS Snowball Edge Storage Optimized with Glue: This device is designed for large data transfers. Copying the data to the device is straightforward, and AWS Glue can handle data transformation in the cloud.

AWS Snowball Edge Storage Optimized with EC2: This involves setting up EC2 instances for transformation, adding operational complexity compared to using AWS Glue.

Best Solution:

AWS Snowball Edge Storage Optimized with AWS Glue: This provides the least operational overhead for transferring large amounts of data and setting up the transformation job in the cloud.

Understanding the Requirement: The company needs to ensure clients use a TLS certificate when accessing the website and automate the creation and renewal of TLS certificates.

Analysis of Options:

CloudFront Security Policy: Not applicable for creating certificates.

CloudFront Origin Access Control (OAC): Controls access to origins, not relevant for TLS certificate creation.

AWS Certificate Manager (ACM) with DNS Validation: Provides automated certificate management, including creation and renewal, with minimal manual intervention. DNS validation is automated and does not require manual intervention as email validation does.

AWS Certificate Manager (ACM) with Email Validation: Requires manual intervention to approve validation emails, which increases operational effort.

Best Solution:

AWS Certificate Manager (ACM) with DNS Validation: Ensures automated and efficient certificate management with the least operational effort.

https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-integrate-with-cognito.html

To control access to the REST API and reduce development efforts, the company can use an Amazon Cognito user pool authorizer in API Gateway. This will allow Amazon Cognito to validate each request and ensure that only authenticated users can access the API. This solution has the LEAST operational overhead, as it does not require the company to develop and maintain any additional infrastructure or code.

Understanding the Requirement: The company needs to collect transaction data in real time, avoid data duplication, and perform additional processing without managing infrastructure.

Analysis of Options:

SQS FIFO Queue with Lambda: Ensures data is processed in order and prevents duplication.Lambda handles processing without the need to manage servers.

SQS FIFO Queue with AWS Batch: While this ensures no duplicates, it introduces additional complexity and management overhead with AWS Batch.

Kinesis Data Streams with AWS Batch and EC2: Involves more components and infrastructure management, which is against the requirement of not wanting to manage infrastructure.

Step Functions with Lambda and EC2: Involves setting up multiple services and still requires managing EC2 instances, increasing complexity.

Best Solution:

SQS FIFO Queue with Lambda: This combination ensures real-time data processing, prevents duplication, and minimizes infrastructure management, meeting all requirements efficiently.

Understanding the Requirement: The job is stateful, cannot tolerate interruptions, and needs to run reliably for at least one hour each week.

Analysis of Options:

AWS Fargate with Amazon ECS and EventBridge: This option provides a serverless compute engine for containers that can run stateful tasks reliably. Using EventBridge Scheduler, the job can be triggered automatically at the specified time without manual intervention.

AWS Lambda with EventBridge: Lambda functions are not suitable for long-running stateful jobs since they have a maximum execution time of 15 minutes.

EC2 Spot Instances: Spot Instances can be interrupted, making them unsuitable for a stateful job that cannot tolerate interruptions.

AWS DataSync: This service is primarily for moving large amounts of data and is not designed to run stateful analysis jobs.

Best Option for Reliable, Scheduled Execution:

The Fargate task on ECS with EventBridge Scheduler meets all requirements, providing the necessary reliability and scheduling capabilities without interruption risks.

Understanding the Requirement: The company needs to automatically detect and respond to suspicious or unexpected behavior in its AWS environment, beyond the existing AWS WAF setup.

Analysis of Options:

Amazon GuardDuty: Provides continuous monitoring and threat detection across AWS accounts and resources, including integration with AWS WAF for automated response.

AWS Firewall Manager: Manages firewall rules across multiple accounts but is more focused on central management than threat detection.

Amazon Inspector: Focuses on security assessments and vulnerability management rather than real-time threat detection.

Amazon Macie: Primarily used for data security and privacy, not comprehensive threat detection.

Best Solution:

Amazon GuardDuty with EventBridge and Lambda: This combination ensures continuous threat detection and automated response by updating AWS WAF rules based on GuardDuty findings.

Understanding the Requirement: The company needs a disaster recovery solution for FSx for NetApp ONTAP in a secondary region, accessible using the same protocols (CIFS and NFS).

Analysis of Options:

Lambda Function and S3: Involves copying data to S3, which changes the access protocols and increases operational overhead.

AWS Backup: Suitable for backup and restore but not for real-time or near-real-time replication for disaster recovery.

FSx for ONTAP with SnapMirror: SnapMirror provides efficient replication between ONTAP instances, maintaining access protocols and requiring minimal operational overhead.

Amazon EFS: Does not support CIFS and requires migrating data, increasing complexity and changing access protocols.

Best Solution:

FSx for ONTAP with SnapMirror: This solution ensures seamless disaster recovery with the same access protocols and minimal operational overhead.

Understanding the Requirement: The robotics company needs a public load balancer to ensure seamless communication with backend services, route traffic based on query strings, and encrypt traffic.

Analysis of Options:

Network Load Balancer with ACM Certificate: NLBs primarily operate at the transport layer (Layer 4) and do not natively support query parameter-based routing, which is a Layer 7 feature.

Gateway Load Balancer with IAM Certificate: Gateway Load Balancers are designed for deploying, scaling, and managing third-party virtual appliances and do not support HTTP path-based or query parameter-based routing.

Application Load Balancer with ACM Certificate: ALBs operate at the application layer (Layer 7), supporting features like query parameter-based routing and SSL/TLS termination with ACM certificates.

Network Load Balancer with IAM Certificate: As with the first option, NLBs do not support query parameter-based routing, making it unsuitable for this requirement.

Best Solution:

Application Load Balancer with ACM Certificate: This option provides the necessary Layer 7 routing capabilities and SSL/TLS termination to meet the requirements for query parameter-based routing and encrypted communication.

Understanding the Requirement: The company needs a container solution that can scale across on-premises, hybrid, and cloud environments, with a load balancer for HTTP traffic.

Analysis of Options:

Fargate Launch Type and ECS Anywhere: Using Fargate for cloud-based containers and ECS Anywhere for on-premises containers provides a unified management experience across environments without needing to manage infrastructure.

Application Load Balancer: Suitable for HTTP traffic and can distribute requests to the ECS services, ensuring scalability and performance.

Network Load Balancer: Typically used for TCP/UDP traffic, not specifically optimized for HTTP traffic.

EC2 Launch Type for ECS and ECS Anywhere with Fargate: Involves managing infrastructure for EC2 instances, increasing operational overhead.

Best Combination of Solutions:

ECS with Fargate Launch Type and ECS Anywhere: This provides flexibility and scalability across hybrid environments with minimal operational overhead.

Application Load Balancer: Optimized for HTTP traffic, ensuring efficient load distribution and scaling for the ECS services.

Understanding the Requirement: The company needs to migrate sensitive and critical data from on-premises SQL Server databases to AWS, aiming to increase security and reduce operational overhead.

Analysis of Options:

EC2 Instances with KMS: Running SQL Server on EC2 provides control but requires significant operational overhead for management, backups, patching, and high availability.

Multi-AZ Amazon RDS for SQL Server with KMS: Amazon RDS for SQL Server offers managed database services, reducing operational overhead. Multi-AZ deployment provides high availability, and KMS encryption ensures data security.

Amazon S3 and Macie: S3 is not a suitable replacement for relational databases, and Macie is used for data security and compliance but not for database operations.

Amazon DynamoDB and CloudWatch Logs: DynamoDB is a NoSQL database and does not support SQL Server workloads directly. CloudWatch Logs are used for monitoring, not for ensuring database security.

Best Solution:

Multi-AZ Amazon RDS for SQL Server with KMS: This solution meets the requirements for security, high availability, and reduced operational overhead by using a managed database service with encryption.

Understanding the Requirement: The application running in a private subnet needs to store and retrieve data from S3 without data traveling over the public internet.

Analysis of Options:

NAT Gateway: Allows private subnets to access the internet but incurs additional costs and still routes traffic through the public internet.

AWS Storage Gateway: Provides hybrid cloud storage solutions but is not the most cost-effective for direct S3 access from within the VPC.

S3 Interface Endpoint: Provides private access to S3 but is generally used for specific use cases where more granular control is required, which might be overkill and more expensive.

S3 Gateway Endpoint: Provides private, cost-effective access to S3 from within the VPC without routing traffic through the public internet.

Best Solution:

S3 Gateway Endpoint: This option meets the requirements for secure, private access to S3 from a private subnet most cost-effectively.

Requirement Analysis: Need quick, cost-effective, and consistent access to on-premises network services from multiple AWS accounts.

AWS Transit Gateway: Centralizes and simplifies network management by connecting VPCs and on-premises networks.

Direct Connect Integration: Assigning DX to the transit gateway ensures consistent and high-performance connectivity.

Operational Overhead: Minimal because Transit Gateway simplifies routing and management.

Implementation:

Set up AWS Transit Gateway.

Connect new AWS accounts to the Transit Gateway.

Route traffic through Transit Gateway to on-premises servers via Direct Connect.

Conclusion: This solution provides a scalable, cost-effective, and low-overhead method to meet connectivity requirements.

References

AWS Transit Gateway:AWS Transit Gateway Documentation

Understanding the Requirement: The company needs logs to be highly available for 30 days for frequent analysis, retained for an additional 60 days for backup, and deleted after 90 days.

Analysis of Options:

Transition to S3 Standard after 30 days: Keeps logs in the same high-availability storage, not cost-effective.

Transition to S3 Standard-IA, then Glacier Flexible Retrieval after 90 days: Adds unnecessary cost and complexity since objects need to be accessible for only 30 days and then retained for 60 days.

Transition to Glacier Flexible Retrieval after 30 days: Not suitable for frequent access required in the first 30 days.

Transition to S3 One Zone-IA after 30 days, then Glacier Flexible Retrieval: Provides cost-effective storage for infrequently accessed logs after the initial 30-day period, then moves to the cheapest long-term storage before deletion.

Best Solution:

Transition to S3 One Zone-IA after 30 days, then Glacier Flexible Retrieval: This solution meets the requirements for high availability, cost-effective storage for backup, and scheduled deletion with the least cost.

Understanding the Requirement: The company wants to cost-optimize their workload that uses EC2, Lambda, Fargate, and SageMaker, covering the most services with the fewest savings plans.

Analysis of Options:

EC2 Instance Savings Plan: Limited to EC2 and SageMaker, missing coverage for Lambda and Fargate.

Compute Savings Plan: Provides the most flexibility, covering a broad range of compute services, including EC2, Lambda, Fargate, and SageMaker.

SageMaker Savings Plan: Specifically for SageMaker, missing coverage for EC2, Lambda, and Fargate.

Combination of plans: The Compute Savings Plan is versatile and can be combined to cover different services efficiently.

Best Solution:

Compute Savings Plan for EC2, Lambda, and SageMaker: Covers the primary compute services efficiently.

Compute Savings Plan for Lambda, Fargate, and EC2: Covers the remaining services, ensuring broad coverage with minimal plans.

Understanding the Requirement: The application running on EC2 instances in private subnets needs to securely connect to an Amazon SQS queue without exposing traffic to the public internet.

Analysis of Options:

Interface VPC Endpoint in Private Subnets: Allows private, secure connectivity to SQS without using the public internet.Configuring security groups ensures controlled access from EC2 instances.

Interface VPC Endpoint in Public Subnets: Not necessary for private EC2 instances and exposes additional security risks.

Gateway Endpoint: Gateway endpoints are not supported for SQS; they are used for services like S3 and DynamoDB.

NAT Gateway with IAM Role: Increases costs and complexity compared to using an interface VPC endpoint directly.

Best Solution:

Interface VPC Endpoint in Private Subnets: This option ensures secure, private connectivity to SQS, meeting the requirement with minimal complexity and optimal security.

Understanding the Requirements: The company needs to optimize costs and has the flexibility to change EC2 instance types and families frequently (every 2-3 months).

Savings Plans Overview: Savings Plans offer significant savings over On-Demand pricing, with the flexibility to use any instance type and family within a region.

No Upfront Compute Savings Plan: This plan allows for cost optimization without any upfront payment, offering flexibility to change instance types and families.

Term Selection: A 1-year term is appropriate for balancing cost savings and flexibility given the frequent changes in instance types.

Conclusion: A No Upfront Compute Savings Plan for a 1-year term provides the needed flexibility and cost savings without the commitment and inflexibility of Reserved Instances.

References

AWS Savings Plans:AWS Savings Plans

AWS Cost Management Documentation:AWS Cost Management

Understanding the Requirement: The company needs to process images as they are uploaded to S3 in a cost-effective manner, currently using an EC2 Spot Fleet for nightly processing.

Analysis of Options:

S3 Event Notifications to SQS and Lambda: This setup allows for event-driven processing with Lambda, which scales automatically based on the number of messages in the queue. It is cost-effective as Lambda charges are based on the compute time used.

S3 Event Notifications to SQS and EC2 Reserved Instance: Involves managing EC2 instances, which adds operational overhead and is less cost-effective.

S3 Event Notifications to SNS and ECS: More complex and potentially less cost-effective compared to using Lambda for simple processing tasks.

S3 Event Notifications to SNS: Requires additional configuration and management to process messages.

Best Solution:

S3 Event Notifications to SQS and Lambda: This option is the most cost-effective and scalable, leveraging AWS managed services with minimal operational overhead.

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/monitor-ami-events.html#:~:text=For%20example%2C%20you%20can%20create%20an%20EventBridge%20rule%20that%20detects%20when%20the%20AMI%20creation%20process%20has%20completed%20and%20then%20invokes%20an%20Amazon%20SNS%20topic%20to%20send%20an%20email%20notification%20to%20you.

Creating an Amazon EventBridge (Amazon CloudWatch Events) rule for the CreateImage API call and configuring the target as an Amazon Simple Notification Service (Amazon SNS) topic to send an alert when a CreateImage API call is detected will meet the requirements with the least operational overhead. Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated Software as a Service (SaaS) applications, and AWS services. By creating an EventBridge rule for the CreateImage API call, the company can set up alerts whenever this operation is called within their account. The alert can be sent to an SNS topic, which can then be configured to send notifications to the company's email or other desired destination.

Understanding the Requirement: The company needs to map its IT infrastructure to identify and enforce security policies, with the ability to quickly query and identify security risks.

Analysis of Options:

Amazon RDS: While suitable for relational data, it is not optimized for handling complex relationships and querying those relationships, which is essential for an IT infrastructure map.

Amazon Neptune: A graph database service designed for handling highly connected data. It uses SPARQL to query graph data efficiently, making it ideal for mapping IT infrastructure and identifying relationships that pose security risks.

Amazon Redshift: A data warehouse solution optimized for complex queries on large datasets but not specifically for graph data.

Amazon DynamoDB: A NoSQL database that uses PartiQL for querying, but it is not optimized for complex relationships in graph data.

Best Option for Mapping and Querying IT Infrastructure:

Amazon Neptune provides the most suitable solution with the least operational overhead. It is purpose-built for graph data and enables efficient querying of complex relationships to identify security risks.

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Replication.html

#Aurora.Replication.Replicas Aurora Replicas have two main purposes. You can issue queries to them to scale the read operations for your application. You typically do so by connecting to the reader endpoint of the cluster. That way, Aurora can spread the load for read-only connections across as many Aurora Replicas as you have in the cluster. Aurora Replicas also help to increase availability. If the writer instance in a cluster becomes unavailable, Aurora automatically promotes one of the reader instances to take its place as the new writer.

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Overview.html

Analysis:

The solution must handle variable sales volumes, preserve transaction information, and store data in an Amazon Aurora database with minimal operational overhead. UsingAPI Gateway, AWS Lambda, and SQSis the best option because it provides scalability, reliability, and resilience.

Why Option A is Correct:

API Gateway: Serves as an entry point for transaction data in a serverless, scalable manner.

AWS Lambda: Processes the transactions and sends them to Amazon SQS for queuing.

Amazon SQS: Buffers the transaction data, ensuring durability and resilience against spikes in transaction volume.

Second Lambda Function: Processes messages from the SQS queue and updates the Aurora database, decoupling the workflow for better scalability.

Dead-Letter Queue (DLQ): Ensures failed transactions are logged for later debugging or reprocessing.

Why Other Options Are Not Ideal:

Option B:

Using an ALB with ECS on EC2 introduces operational overhead, such as managing EC2 instances and scaling ECS tasks.Not cost-effective.

Option C:

EKS is highly operationally intensive and requires Kubernetes cluster management, which is unnecessary for this use case.Too complex.

Option D:

Amazon Data Firehose and DMS are not designed for real-time transactional workflows. They are better suited for data analytics pipelines.Not suitable.

AWS References:

Amazon API Gateway:AWS Documentation - API Gateway

AWS Lambda:AWS Documentation - Lambda

Amazon SQS:AWS Documentation - SQS

Amazon Aurora:AWS Documentation - Aurora

Migrating to Amazon MQ reduces the overhead on the queue management. C and D are dismissed. Deciding between A and B means deciding to go for an AutoScaling group for EC2 or an RDS for Postgress (both multi- AZ). The RDS option has less operational impact, as provide as a service the tools and software required. Consider for instance, the effort to add an additional node like a read replica, to the DB. https://docs.aws.amazon.com/amazon-mq/latest/developer-guide/active-standby-broker-deployment.htmlhttps://aws.amazon.com/rds/postgresql/

Requirement Analysis: The application must process each credit card data validation request at least once, securely and cost-effectively.

SQS FIFO Queues: Ensures that each message is processed exactly once and in the exact order sent.

AWS Lambda: Using Lambda for event-driven processing ensures scalability and cost-efficiency.

SSE-SQS: Provides encryption at rest using SQS-managed keys, simplifying encryption management.

Implementation:

Set up SQS FIFO queues as the event source for Lambda.

Enable SSE-SQS for encryption.

Ensure the Lambda execution role has the necessary permissions to use the encryption keys.

Conclusion: This combination meets the requirements of security, exact-once processing, and cost-effectiveness.

References

Amazon SQS:Amazon SQS Documentation

AWS Lambda with SQS:Using AWS Lambda with Amazon SQS

https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.htmlhttps://docs.aws.amazon.com/IAM/latest/UserGuide/id_users.html

This solution meets the requirements of creating digital copies for a large collection of historical written records, analyzing the documents, extracting the medical information, and storing the documents so that an application can run SQL queries on the data. Writing the document information to an Amazon S3 bucket can provide scalable and durable storage for the scanned files. Using Amazon Athena to query the data can provide serverless and interactive SQL analysis on data stored in S3. Creating an AWS Lambda function that runs when new documents are uploaded can provide event-driven and serverless processing of the scanned files. Using Amazon Textract to convert the documents to raw text can provide accurate optical character recognition (OCR) and extraction of structured data such as tables and forms from documents using artificial intelligence (AI). Using Amazon Comprehend Medical to detect and extract relevant medical information from the text can provide natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text.

Option A is incorrect because writing the document information to an Amazon EC2 instance that runs a MySQL database can increase the infrastructure overhead and complexity, and it may not be able to handle large volumes of data. Option C is incorrect because creating an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information can increase the infrastructure overhead and complexity, and it may not be able to leverageexisting AI and NLP services such as Textract and Comprehend Medical. Option D is incorrect because using Amazon Rekognition to convert the documents to raw text can provide image and video analysis, but it does not support OCR or extraction of structured data from documents. Using Amazon Transcribe Medical to detect and extract relevant medical information from the text can provide speech-to-text transcription service for medical conversations, but it does not support text analysis or extraction of health data from medical text.

Amazon RDS Proxy is designed to manage a large number of database connections from applications, especially serverless applications that can scale quickly. It improves application availability and scalability by pooling and sharing established database connections. This reduces the overhead of database connections and prevents overload during traffic spikes.

Understanding the Requirement: The company anticipates a spike in traffic during a holiday and wants to ensure the Auto Scaling group can handle the increased load without impacting performance.

Analysis of Options:

CloudWatch Alarm: This reacts to spikes based on metrics like CPU utilization but does not proactively scale before the anticipated demand.

Recurring Scheduled Action: This allows the Auto Scaling group to scale up based on a known schedule, ensuring additional capacity is available before the expected spike.

Increase Min/Max Instances: This could result in unnecessary costs by maintaining higher capacity even when not needed.

SNS Notification: Alerts on scaling events but does not proactively manage scaling to prevent performance issues.

Best Solution for Proactive Scaling:

Create a recurring scheduled action: This approach ensures that the Auto Scaling group scales up before the peak demand, providing the necessary capacity proactively without manual intervention.

This approach ensures that the order creation and payment processing steps are separate and atomic. By sending the order information to an SQS FIFO queue, the payment service can process the order one at a time and in the order they were received. If the payment service is unable to process an order, it can be retried later, preventing the creation of multiple orders. The deletion of the message from the queue after it is processed will prevent the same message from being processed multiple times.

Understanding the Requirement: The company needs to redesign the architecture to be highly available and use AWS managed solutions for hosting a web application with static content, PHP application, and Redis for user sessions.

Analysis of Options:

AWS Elastic Beanstalk: Suitable for simplifying deployment but may not provide the desired flexibility and control for complex architectures.

AWS Lambda and API Gateway: Not ideal for hosting a stateful PHP application and handling static content.Adding complexity without significant benefit.

EC2 instance with ElastiCache and S3: Provides some high availability but involves managing EC2 instances, which increases operational overhead.

CloudFront with S3, ALB, ECS with Fargate, and ElastiCache: This solution leverages fully managed AWS services for each component, ensuring high availability and scalability.

Best Solution:

CloudFront with S3, ALB, ECS with Fargate, and ElastiCache: This combination of services meets the requirements for a highly available and managed solution, ensuring optimal performance and minimal operational overhead.

A. On-premises tool via internet:Incurs high costs due to large data transfers over the internet.

B. AWS Region tool via internet:Does not utilize Direct Connect, leading to potential latency and higher costs.

C. On-premises tool via Direct Connect:Adds latency for querying and visualization.

D. AWS Region tool via Direct Connect:Reduces latency and leverages Direct Connect for optimized data transfer costs.

https://computingforgeeks.com/stream-logs-in-aws-from-cloudwatch-to-elasticsearch/

Cloudfront for rapid response and s3 to minimize infrastructure.

Understanding the Requirement: The application experiences slow performance at the start of peak hours, but normalizes after a few hours. The goal is to ensure proper performance at the beginning of peak hours.

Analysis of Options:

Application Load Balancer: Ensures proper traffic distribution but does not address the need to have sufficient instances running at the start of peak hours.

Dynamic Scaling Policy Based on Memory or CPU Utilization: While dynamic scaling reacts to usage metrics, it may not preemptively scale in anticipation of peak hours, leading to delays as new instances are launched and become available.

Scheduled Scaling Policy: This allows the Auto Scaling group to launch instances ahead of time, ensuring that enough instances are available and ready to handle the increased load right at the start of peak hours.

Best Solution:

Scheduled Scaling Policy: This approach ensures that new instances are launched and ready before peak hours begin, addressing the slow performance issue at the start of peak periods.

In compliance mode, a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account. When an object is locked in compliance mode, its retention mode can't be changed, and its retention period can't be shortened. Compliance mode helps ensure that an object version can't be overwritten or deleted for the duration of the retention period. In governance mode, users can't overwrite or delete an object version or alter its lock settings unless they have special permissions. With governance mode, you protect objects against being deleted by most users, but you can still grant some users permission to alter the retention settings or delete the object if necessary. In Governance mode, Objects can be deleted by some users with special permissions, this is against the requirement.

Compliance:

- Object versions can't be overwritten or deleted by any user, including the root user

- Objects retention modes can't be changed, and retention periods can't be shortened

Governance:

- Most users can't overwrite or delete an object version or alter its lock settings

- Some users have special permissions to change the retention or delete the object

Many applications, including those built on modern serverless architectures, can have a large number of open connections to the database server and may open and close database connections at a high rate, exhausting database memory and compute resources. Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability.https://aws.amazon.com/id/rds/proxy/

https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

This solution meets the requirements of measuring the effectiveness of marketing campaigns by performing batch processing on csv files of sales data and storing the results in an Amazon S3 bucket once every hour. An AWS duo ETL process can use services such as AWS Glue or AWS Data Pipeline to extract data from S3, transform it into a more efficient format such as Apache Parquet, and load it back into S3. Apache Parquet is a columnar storage format that can improve the query performance and reliability of Athena by reducing the amount of data scanned, improving compression ratio, and enabling predicate pushdown.

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/dns-failover-types.html

To make all the data available to various teams and minimize operational overhead, the company can create a data lake by using AWS Lake Formation. This will allow the company to centralize all the data in one place and use fine-grained access controls to manage access to the data. To meet the requirements of the company, the solutions architect can create a data lake by using AWS Lake Formation, create an AWS Glue JDBC connection to Amazon RDS, and register the S3 bucket in Lake Formation. The solutions architect can then use Lake Formation access controls to limit access to the data. This solution will provide the ability to manage fine-grained permissions for the data and minimize operational overhead.

Requirement Analysis: The application involves batch processing of large data transfers between EC2 instances.

Data Transfer Costs: Data transfer within the same Availability Zone (AZ) is typically free, while cross-AZ transfers incur additional costs.

Implementation:

Launch all EC2 instances within the same Availability Zone.

Ensure the instances are part of the same subnet to facilitate seamless data transfer.

Conclusion: Placing all EC2 instances in the same AZ reduces data transfer costs significantly without affecting the application’s functionality.

References

AWS Pricing:AWS Data Transfer Pricing

This solution meets the requirements of a customer-facing application that has a clearly defined access pattern throughout the year and a variable number of reads and writes that depend on the time of year. Amazon DynamoDB is a fully managed NoSQL database service that can handle any level of request traffic and data size. DynamoDB auto scaling can automatically adjust the provisioned read and write capacity based on the actual workload. DynamoDB on-demand backups can create full backups of the tables for data protection and archival purposes. DynamoDB Streams can capture a time-ordered sequence of item-level modifications in the tables for audit purposes.

Option B is incorrect because Amazon Redshift is a data warehouse service that is designed for analytical workloads, not for customer-facing applications. Option C is incorrect because Amazon RDS with Provisioned IOPS can provide consistent performance for relational databases, but it may not be able to handle unpredictable spikes in traffic and data size. Option D is incorrect because Amazon Aurora MySQL with auto scaling can provide high performance and availability for relational databases, but it does not support audit logging as a parameter.

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html

This solution meets the requirements of a serverless solution, encryption, replication, and SQL analysis with the least operational overhead. Amazon Athena is a serverless interactive query service that can analyze data in S3 using standard SQL. S3 Cross-Region Replication (CRR) can replicate encrypted objects to an S3 bucket in another Region automatically. Server-side encryption with AWS KMS multi-Region keys (SSE-KMS) can encrypt the data at rest using keys that are replicated across multiple Regions. Creating a new S3 bucket can avoid potential conflicts with existing data or configurations.

Option B is incorrect because Amazon RDS is not a serverless solution and it cannot query data in S3 directly. Option C is incorrect because server-side encryption with Amazon S3 managed encryption keys (SSE-S3) does not use KMS keys and it does not support multi-Region replication. Option D is incorrect because Amazon RDS is not a serverless solution and it cannot query data in S3 directly. It is also incorrect for the same reason as option C.

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/AmazonEFS.html Amazon FSx for Windows File Server provides fully managed Microsoft Windows file servers, backed by a fully native Windows file system.https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.html

To ensure that Amazon S3 buckets do not have unauthorized configuration changes, a solutions architect should turn on AWS Config with the appropriate rules. AWS Config is a service that allows users to audit and assess their AWS resource configurations for compliance with industry standards and internal policies. It provides a detailed view of the resources and their configurations, including information on how the resources are related to each other. By turning on AWS Config with the appropriate rules, users can identify and remediate unauthorized configuration changes to their Amazon S3 buckets.

https://docs.aws.amazon.com/ses/latest/dg/send-email-formatted.html

D. Create an Amazon EventBridge (Amazon CloudWatch Events) scheduled event that invokes an AWS Lambda function to query the application's API for the data. This step can be done using AWS Lambda to extract the shipping statistics and organize the data into an HTML format.

B. Use Amazon Simple Email Service (Amazon SES) to format the data and send the report by email. This step can be done by using Amazon SES to send the report to multiple email addresses at the same time every morning.

Therefore, options D and B are the correct choices for this question. Option A is incorrect because Kinesis Data Firehose is not necessary for this use case. Option C is incorrect because AWS Glue is not required to query the application's API. Option E is incorrect because S3 event notifications cannot be used to send the report by email.

The storage solution that will meet these requirements most cost-effectively is B: Create an S3 Lifecycle configuration to transition objects from S3 Standard to S3 Glacier Deep Archive after 1 month. Amazon S3 Glacier Deep Archive is a secure, durable, and extremely low-cost Amazon S3 storage class for long-term retention of data that is rarely accessed and for which retrieval times of several hours are acceptable. It is the lowest-cost storage option in Amazon S3, making it a cost-effective choice for storing backup files that are not accessed after 1 month. You can use an S3 Lifecycle configuration to automatically transition objects from S3 Standard to S3 Glacier Deep Archive after 1 month. This will minimize the storage costs for the backup files that are not accessed frequently.

**AWS PrivateLink provides private connectivity between VPCs, AWS services, and your on-premises networks, without exposing your traffic to the public internet**. AWS PrivateLink makes it easy to connect services across different accounts and VPCs to significantly simplify your network architecture. Interface **VPC endpoints**, powered by AWS PrivateLink, connect you to services hosted by AWS Partners and supported solutions available in AWS Marketplace. https://aws.amazon.com/privatelink/

This solution meets the requirements of running a gaming application that transmits data by using UDP packets and scaling out and in as traffic increases and decreases. A Network Load Balancer can handle millions of requests per second while maintaining high throughput at ultra low latency, and it supports both TCP and UDP protocols. An Auto Scaling group can automatically adjust the number of EC2 instances based on the demand and the scaling policies.

Option B is incorrect because an Application Load Balancer does not support UDP protocol, only HTTP and HTTPS. Option C is incorrect because Amazon Route 53 is a DNS service that can route traffic based on different policies, but it does not provide load balancing or scaling capabilities. Option D is incorrect because a NAT instance is used to enable instances in a private subnet to connect to the internet or other AWS services, but it does not provide load balancing or scaling capabilities.

AWS Global Accelerator and Amazon CloudFront are separate services that use the AWS global network and its edge locations around the world. CloudFront improves performance for both cacheable content (such as images and videos) and dynamic content (such as API acceleration and dynamic site delivery). Global Accelerator improves performance for a wide range of applications over TCP or UDP by proxying packets at the edge to applications running in one or more AWS Regions. Global Accelerator is a good fit for non-HTTP use cases, such as gaming (UDP), IoT (MQTT), or Voice over IP, as well as for HTTP use cases that specifically require static IP addresses or deterministic, fast regional failover. Both services integrate with AWS Shield for DDoS protection.

SSE-S3 - is free and uses AWS owned CMKs (CMK = Customer Master Key). The encryption key is owned and managed by AWS, and is shared among many accounts. Its rotation is automatic with time that varies as shown in the table here. The time is not explicitly defined.

SSE-KMS - has two flavors:

AWS managed CMK. This is free CMK generated only for your account. You can only view it policies and audit usage, but not manage it. Rotation is automatic - once per 1095 days (3 years),

Customer managed CMK. This uses your own key that you create and can manage. Rotation is not enabled by default. But if you enable it, it will be automatically rotated every 1 year. This variant can also use an imported key material by you. If you create such key with an imported material, there is no automated rotation. Only manual rotation.

SSE-C - customer provided key. The encryption key is fully managed by you outside of AWS. AWS will not rotate it.

This solution meets the requirements of moving data to an Amazon S3 bucket, encrypting the data when it is stored in the S3 bucket, and automatically rotating the encryption key every year with the least operational overhead. AWS Key Management Service (AWS KMS) is a service that enables you to create and manage encryption keys for your data. A customer managed key is a symmetric encryption key that you create and manage in AWS KMS. You can enable automatic key rotation for a customer managed key, which means that AWS KMS generates new cryptographic material for the key every year. You can set the S3 bucket’s default encryption behavior to use the customer managed KMS key, which means that any object that is uploaded to the bucket without specifying an encryption method will be encrypted with that key.

Option A is incorrect because using server-side encryption with Amazon S3 managed encryption keys (SSE-S3) does not allow you to control or manage the encryption keys. SSE-S3 uses a unique key for each object, and encrypts that key with a master key that is regularly rotated by S3. However, you cannot enable or disable key rotation for SSE-S3 keys, or specify the rotation interval. Option C is incorrect because manually rotating the KMS key every year can increase the operational overhead and complexity, and it may not meet the requirement of rotating the key every year if you forget or delay the rotationprocess. Option D is incorrect because encrypting the data with customer key material before moving the data to the S3 bucket can increase the operational overhead and complexity, and it may not provide consistent encryption for all objects in the bucket. Creating a KMS key without key material and importing the customer key material into the KMS key can enable you to use your own source of random bits to generate your KMS keys, but it does not support automatic key rotation.

https://aws.amazon.com/about-aws/whats-new/2018/04/announcing-s3-one-zone-infrequent-access-a-new-amazon-s3-storage-class/?nc1=h_ls

Amazon Aurora is a fully managed, scalable, and highly available relational database service that is compatible with PostgreSQL. Migrating the database to Amazon Aurora would reduce the operational overhead of maintaining the database infrastructure and allow the company to focus on building and scaling the application. AWS Fargate is a fully managed container orchestrationservice that enables users to run containers without the need to manage the underlying EC2 instances. By using AWS Fargate with Amazon Elastic Container Service(Amazon ECS), the solutions architect can improve the scalability and efficiency of the web application and reduce the operational overhead of maintaining the underlying infrastructure.

ElastiCache can help speed up the read performance of the database by caching frequently accessed data, reducing latency and allowing the application to access the data more quickly. This solution requires minimal modifications to the current architecture, as ElastiCache can be used in conjunction with the existing Amazon RDS for MySQL database.

The details are revealed in below url:https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/FIFO-queues.html

FIFO (First-In-First-Out) queues are designed to enhance messaging between applications when the order of operations and events is critical, or where duplicates can't be tolerated. Examples of situations where you might use FIFO queues include the following: To make sure that user-entered commands are run in the right order. To display the correct product price by sending price modifications in the right order. To prevent a student from enrolling in a course before registering for an account.

https://aws.amazon.com/kms/faqs/#:~:text=If%20you%20are%20a%20developer%20who%20needs%20to%20digitally,a%20broad%20set%20of%20industry%20and%20regional%20compliance%20regimes.

Migrate the database to Amazon Aurora MySQL - this will let the DB scale on it's own; it'll scale automatically without needing adjustment. Create AMI of the web app and using a launch template - this will make the creating of any future instances of the app seamless. They can then be added to the auto scaling group which will save them money as it will scale up and down based on demand. Using a spot fleet to launch instances- This solves the "MOST cost-effective" portion of the question as spot instances come at a huge discount at the cost of being terminated at any time Amazon deems fit. I think this is why there's a bit of disagreement on this. While it's the most cost effective, it would be a terrible choice if amazon were to terminate that spot instance during a busy period.

https://aws.amazon.com/blogs/networking-and-content-delivery/deliver-your-apps-dynamic-content-using-amazon-cloudfront-getting-started-template/

1. database shows no signs of being overloaded. CPU, memory, and disk access metrics are all low==>A and C out. We cannot only add nodes instance or add read replica, because database workload is totally fine, very low. 2. "least operational overhead"==>B out, because b need to configure lambda. 3. ROS proxy: Shares infrequently used connections; High availability with failover; Drives increased efficiency==>proxy can leverage failover to redirect traffic from timeout rds instance to healthy rds instance. So D is right.

https://aws.amazon.com/blogs/compute/building-loosely-coupled-scalable-c-applications-with-amazon-sqs-and-amazon-sns/

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-dead-letter-queues.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/distribution-web-awswaf.html

https://aws.amazon.com/premiumsupport/knowledge-center/s3-private-connection-no-authentication/

This solution meets the requirements of moving the files automatically, running Lambda functions on the copied data, and sending the data files to SageMaker Pipelines with the least operational overhead. S3 replication can copy the files from the initial S3 bucket to the analysis S3 bucket as they arrive. The analysis S3 bucket can send event notifications to Amazon EventBridge (Amazon CloudWatch Events) when an object is created.EventBridge can trigger Lambda and SageMaker Pipelines as targets for the ObjectCreated rule. Lambda can run pattern-matching code on the copied data, and SageMaker Pipelines can execute a pipeline with the data files.

Option A is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option B is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option C is incorrect because using S3 event notification with multiple destinations can result in throttling or delivery failures if there are too many events.

EC2 instance Savings Plan saves 72% while Compute Savings Plans saves 66%. But according to link, it says "Compute Savings Plans provide the most flexibility and help to reduce your costs by up to 66%. These plans automatically apply to EC2 instance usage regardless of instance family, size, AZ, region, OS or tenancy, and also apply to Fargate and Lambda usage." EC2 instance Savings Plans are not applied to Fargate or Lambda

To meet the new requirement of transferring files over a private route, the EC2 instances should be moved to private subnets, which do not have direct access to the internet. This ensures that the traffic for file transfers does not go over the internet. To enable the EC2 instances to access Amazon S3, a VPC endpoint for Amazon S3 can be created. VPC endpoints allow resources within a VPC to communicate with resources in other services without the traffic being sent overthe internet. By linking the VPC endpoint to the route table for the private subnets, the EC2 instances can access Amazon S3 over a private connection within the VPC.

https://aws.amazon.com/premiumsupport/knowledge-center/cloudwatch-memory-metrics-ec2/

The visibility timeout begins when Amazon SQS returns a message. During this time, the consumer processes and deletes the message. However, if the consumer fails before deleting the message and your system doesn't call the DeleteMessage action for that message before the visibility timeout expires, the message becomes visible to other consumers and the message is received again. If a message must be received only once, your consumer should delete it within the duration of the visibility timeout.https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-visibility-timeout.html

Keyword: SQS queue writes to an Amazon RDS From this, Option D best suite & other Options ruled out [Option A - You can't intruduce one more Queue in the existing one; Option B - only Permission & Option C - Only Retrieves Messages] FIF O queues aredesigned to never introduce duplicate messages. However, your message producer might introduce duplicates in certain scenarios: for example, if the producer sends a message, does not receive a response, and then resends the same message. Amazon SQS APIs provide deduplication functionality that prevents your message producer from sending duplicates. Any duplicates introduced by the message producer are removed within a 5-minute deduplication interval. For standard queues, you might occasionally receive a duplicate copy of a message (at-least- once delivery). If you use a standard queue, you must design your applications to be idempotent (that is, they must not be affected adversely when processing the same message more than once).

Composite alarms determine their states by monitoring the states of other alarms. You can **use composite alarms to reduce alarm noise**. For example, you can create a composite alarm where the underlying metric alarms go into ALARM when they meet specific conditions. You then canset up your composite alarm to go into ALARM and send you notifications when the underlying metric alarms go into ALARM by configuring the underlying metric alarms never to take actions. Currently, composite alarms can take the following actions:https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Create_Composite_Alarm.html

https://docs.aws.amazon.com/whitepapers/latest/aws-vpc-connectivity-options/aws-direct-connect-aws-transit-gateway.html

https://www.omnicalculator.com/other/data-transfer

The basic difference between Snowball and Snowball Edge is the capacity they provide. Snowball provides a total of 50 TB or 80 TB, out of which 42 TB or 72 TB is available, while Amazon Snowball Edge provides 100 TB, out of which 83 TB is available.

This solution meets the requirements of a two-tier application that has a variable demand based on the time of day and must be available at all times, while minimizing the overall cost. EC2 Reserved Instances can provide significant savings compared to On-Demand Instances for the baseline level of usage, and they can guarantee capacity reservation when needed. EC2 Spot Instances can provide up to 90% savings compared to On-Demand Instances for any additional capacity that the application needs during peak hours. Spot Instances are suitable for stateless applications that can tolerate interruptions and can be replaced by other instances. Stopping the RDS database when it is not in use can reduce the cost of running the database tier.

Option A is incorrect because using all EC2 Spot Instances can affect the availability of the application if there are not enough spare capacity or if the Spot price exceeds the maximum price. Stopping the RDS database when it is not in use can reduce the cost of running the database tier, but it can also affect the availability of the application. Option B is incorrect because purchasing EC2 Instance Savings Plans to cover five EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Purchasing an RDS Reserved DB Instance can provide savings for the database tier, but it does not allow stopping the database when it is not in use. Option D is incorrect becausepurchasing EC2 Instance Savings Plans to cover two EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Using up to three additional EC2 On-Demand Instances as needed can incur higher costs than using Spot Instances.

Amazon Athena can be used to query JSON in S3

To achieve high availability with minimum downtime and minimum loss of data, the Auto Scaling group should be configured to use multiple Availability Zones to ensure that there is no single point of failure. The database should be configured as Multi-AZ to enable automatic failover in case of an outage in the primary Availability Zone. Additionally, an Amazon RDSProxy instance can be used to improve the scalability and availability of the database by reducing connection failures and improving failover times.

Share the existing KMS key with the MSP external account because it has already been used to encrypt the AMI snapshot.https://docs.aws.amazon.com/kms/latest/developerguide/key-policy-modifying-external-accounts.html

Static content can be cached at Cloud front Edge locations from S3 and dynamic content EC2 behind the ALB whose performance can be improved by Global Accelerator whose one endpoint is ALB and other Cloud front. So with regards to custom domain name endpoint is web application is R53 alias records for the custom domain point to web applicationhttps://aws.amazon.com/blogs/networking-and-content-delivery/improving-availability-and-performance-for-application-load-balancers-using-one-click-integration-with-aws-global-accelerator/

To reduce the cost of running the tests without reducing the compute and memory attributes of the Amazon RDS for MySQL DB instance, the development team can stop the instance when tests are completed and restart it when required. Stopping the DB instance when not in use can help save costs because customers are only charged for storage while the DB instance is stopped. During this time, automated backups and automated DB instance maintenance are suspended.When the instance is restarted, it retains the same configurations, security groups, and DB parameter groups as when it was stopped.

"Typically, you configure buckets to be Requester Pays buckets when you want to share data but not incur charges associated with others accessing the data. For example, you might use Requester Pays buckets when making available large datasets, such as zip code directories, reference data, geospatial information, or web crawling data."https://docs.aws.amazon.com/AmazonS3/latest/userguide/RequesterPaysBuckets.html

Dynamic scaling is a type of autoscaling that automatically adjusts the number of EC2 instances in an Auto Scaling group based on demand or load. It uses CloudWatch alarms to trigger scaling actions when a specified metric crosses a threshold. It can scale out (addinstances) or scale in (remove instances) as needed1. By using dynamic scaling, the solution can maintain application performance during sudden traffic increases most cost-effectively.

A. Use manual scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of maintaining application performance during sudden traffic increases, as manual scaling requires users to manually increase or decrease the number of instances through a CLI or console. It does not respond automatically to changes in demand or load2.

B. Use predictive scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of most cost-effectiveness, as predictive scaling uses machine learning and artificial intelligence tools to evaluate traffic loads and anticipate when more or fewer resources are needed. It performs scheduled scaling actions based on the prediction, which may not matchthe actual demand or load at any given time. Predictive scaling is more suitable for scenarios where there are predictable traffic patterns or known changes in traffic loads3.

D. Use schedule scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of maintaining application performance during sudden traffic increases, as schedule scaling performs scaling actions at specific times that users schedule. It does not respond automatically to changes in demand or load. Schedule scaling is more suitable for scenarios where there are predictable traffic drops or spikes at specific times of the day.

Reference URL: https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scale-based-on-demand.html

https://docs.aws.amazon.com/systems-manager/latest/userguide/about-windows-app-patching.html

To meet the requirements of the company to have access to both AWS and on-premises file storage with minimum latency, a hybrid cloud architecture can be used. One solution is to deploy and configure Amazon FSx for Windows File Server on AWS, which provides fully managed Windows file servers. The on-premises file data can be moved to the FSx File Gateway, which can act as a bridge between on-premises and AWS file storage. The cloud workloads can be configured to use FSx for Windows File Server on AWS, while the on-premises workloads can be configured to use the FSx File Gateway. This solution minimizes operational overhead and requires no significant changes to the existing file access patterns. The connectivity between on-premises and AWS can be established using an AWS Site-to-Site VPN connection.

https://aws.amazon.com/getting-started/hands-on/build-serverless-web-app-lambda-apigateway-s3-dynamodb-cognito/module-4/

Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, AWS Amplify, Amazon DynamoDB, and Amazon Cognito. This example showed similar setup as question: Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, AWS Amplify, Amazon DynamoDB, and Amazon Cognito

https://aws.amazon.com/blogs/aws/amazon-aurora-fast-database-cloning/

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

HTTP /HTTPS - ALB ; TCP and UDP - NLB; Lowest latency routing and more throughput. Also supports failover, uses Anycast Ip addressing - Global Accelerator Caching at Egde Locations – Cloutfront

WS Global Accelerator automatically checks the health of your applications and routes user traffic only to healthy application endpoints. If the health status changes or you make configuration updates, AWS Global Accelerator reacts instantaneously to route your users to the next available endpoint..

Key Requirements:

Handle traffic spikes efficiently and reduce latency caused by cold starts.

Optimize costs during low traffic periods.

Analysis of Options:

Option A:

Provisioned Concurrency:Reduces cold start latency by pre-warming Lambda environments for the required number of concurrent executions.

AWS Application Auto Scaling:Automatically adjusts provisioned concurrency based on demand, ensuring cost optimization by scaling down during low traffic.

Correct Approach:Provides a balance between performance during traffic spikes and cost optimization during idle periods.

Option B:

Using EC2 instances with Auto Scaling introduces unnecessary complexity for a serverless architecture. It requires additional management and does not address the issue of cold starts for Lambda.

Incorrect Approach:Contradicts the serverless design philosophy and increases operational overhead.

Option C:

Setting a fixed concurrency level ensures performance during spikes but does not optimize costs during low traffic. This approach would maintain provisioned instances unnecessarily.

Incorrect Approach:Lacks cost optimization.

Option D:

Using EventBridge Scheduler for periodic invocations may reduce cold starts but does not dynamically scale based on traffic demand. It also leads to unnecessary invocations during idle times.

Incorrect Approach:Suboptimal for high traffic fluctuations and cost control.

AWS Solution Architect References:

AWS Lambda Provisioned Concurrency

AWS Application Auto Scaling with Lambda

The solution that meets the requirements with the most operational efficiency is to configure public subnets in the existing VPC and deploy an MSK cluster in the public subnets. This solution allows the data ingestion solution to be publicly available over the internet without creating a new VPC or deploying a load balancer. The solution also ensures that the data intransit is encrypted by enabling mutual TLS authentication, which requires both the client and the server to present certificates for verification. This solution leverages the public access feature of Amazon MSK, which is available for clusters running Apache Kafka 2.6.0 or later versions1.

The other solutions are not as efficient as the first one because they either create unnecessary resources or do not encrypt the data in transit. Creating a new VPC with public subnets would incur additional costs and complexity for managing network resources and routing. Deploying an ALB or an NLB would also add more costs and latency for the data ingestion solution. Moreover, an ALB or an NLB would not encrypt thedata in transit by itself, unless they are configured with HTTPS listeners and certificates, which would require additional steps and maintenance. Therefore, these solutions are not optimal for the given requirements.

This solution meets the following requirements:

It is the least effort, as it does not require any additional AWS services, custom scripts, or data processing steps. Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. You can use Athena to query CloudTrail logs directly from the S3 bucket where they are stored, without any data loading or transformation. You can also use the AWS Management Console, the AWS CLI, or the Athena API to run and manage your queries.

It is effective, as it allows you to filter, aggregate, and join CloudTrail log data using SQL syntax. You can use various SQL functions and operators to specify the criteria for identifyingAccess Denied and Unauthorized errors, such as the error code, the user identity, the event source, the event name, the event time, and the resource ARN. You can also use subqueries, views, and common table expressions to simplify and optimize your queries.

It is flexible, as it allows you to customize and save your queries for future use. You can also export the query results to other formats, such as CSV or JSON, or integrate them with other AWS services, such as Amazon QuickSight, for further analysis and visualization.

AWS Glue DataBrew is a visual data preparation tool that allows you to easily clean, normalize, and transform your data without writing any code. You can create and run data preparation jobs on your data stored in Amazon S3, Amazon Redshift, or other data sources. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use Step Functions to orchestrate your DataBrew jobs, define the order and parallelism of execution, handle errors and retries, and monitor the state of your workflow. By using AWS Glue DataBrew and AWS Step Functions, you can meet the requirements of the company with minimal operational overhead, as you do not need to write any code, manage any servers, or deal with complex dependencies.

This solution meets the requirements because it requires the least amount of infrastructure management and guarantees exactly-once delivery for application messaging. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You only pay for the compute time you consume.Lambda scales automatically with the size of your workload. Amazon SQS FIFO queues are designed to ensure that messages are processed exactly once, in the exact order that they are sent. FIFO queues have high availability and deliver messages in a strict first-in, first-out order. You can use Amazon SQS to decouple and scale microservices, distributed systems, and serverless applications. References: AWS Lambda, Amazon SQS FIFO queues

This solution meets the requirements with the least operational overhead because it leverages the features of IAM Identity Center and AWS Control Tower to centrally manage multiple user permissions across all the accounts. By creating new groups and permission sets, the company can assign fine-grained permissions to the developer and administrator teams based on their roles and responsibilities. The permission sets are applied to the groups at the organization level, so they are automatically inherited by all the accounts in the organization. When new users are hired, the company only needs to add them to the appropriate group in IAM Identity Center, and they will automatically get the permissions assigned to that group. This simplifies the user management and reduces the manual effort of assigning permissions to each user individually.

This option is the only solution that meets the requirements because it allows the company to encrypt the data with its own encryption keys and tools outside the AWS Cloud. By encrypting the data at the company’s data center before storing the data in the S3 bucket, the company can ensure that the data is encrypted in transit and at rest, and that the company has full control over the encryption keys and processes. This option also avoids the need to use any AWS encryption services or features, which may not be compatible with the company’s security policies or compliance standards.

A. Encrypt the data in the S3 bucket with server-side encryption (SSE) that uses an AWS Key Management Service (AWS KMS) customer managed key. This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. Although the company can create and use its own customer managed key in AWSKMS, the key is still stored and managed by AWS KMS, which is a service within the AWS Cloud. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company’s security policies or compliance standards.

B. Encrypt the data in the S3 bucket with server-side encryption (SSE) that uses an AWS Key Management Service (AWS KMS) AWS managed key. This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. In this option, the company uses the default AWS managed key in AWS KMS, which is created and managed by AWS on behalf of the company. The company has no control over the key rotation, deletion, or recovery policies. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company’s security policies or compliance standards.

C. Encrypt the data in the S3 bucket with the default server-side encryption (SSE). This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. In this option, the company uses the default server-side encryption with Amazon S3 managed keys (SSE-S3), which is applied to every bucket in Amazon S3. The company has no visibility or control over the encryption keys, which are managed by Amazon S3. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company’s security policies or compliance standards.

 S3 Lifecycle is a feature that allows you to automate the management of your S3 objects based on predefined rules. You can use S3 Lifecycle to delete expired object versions and retain the two most recent versions by creating a lifecycle configuration rule that applies to all objects in the bucket and specifies the expiration action for noncurrent versions. This way, you can reduce the storage costs of your S3 bucket without requiring any application changes or manual intervention. S3 Lifecycle runs once a day and marks the eligible object versions for deletion. You are no longer charged for the objects that are marked for deletion. S3 Lifecycle is the most cost-effective and simple solution among the options.

B. Use an AWS Lambda function to check for older versions and delete all but the two most recent versions. This option is not optimal because it requires you to write, test, and maintain a custom Lambda function that scans the S3 bucket for older versions and deletes them. This can incur additional costs for Lambda invocations and increase the operational complexity and overhead. Moreover, you need to ensure that your Lambda function has the appropriate permissions and error handling mechanisms to perform the deletion operation.

C. Use S3 Batch Operations to delete noncurrent object versions and retain only the two most recent versions. This option is not ideal because S3 Batch Operations is designed for performing large-scale operations on S3 objects, such as copying, tagging, restoring, or invoking a Lambda function. To use S3 Batch Operations to delete noncurrent object versions, you need to provide a manifest file that lists the object versions that you want to delete. This can be challenging and time-consuming to generate and update. Moreover, S3 Batch Operations charges you for each operation that you perform, which can increase your costs.

D. Deactivate versioning on the S3 bucket and retain the two most recent versions. This option is not feasible because deactivating versioning on an S3 bucket does not delete the existing object versions. Instead, it prevents new versions from being created. Therefore, you still need to delete the older versions manually or use another method to do so. Additionally, deactivating versioning can compromise the data protection and recovery capabilities of your S3 bucket.

To ensure that the shopping cart data is preserved at all times, a solutions architect should configure Amazon ElastiCache for Redis to cache catalog data from Amazon DynamoDB and shopping cart data from the user’s session. This solution has the following benefits:

It offers the lowest possible latency from the application, as ElastiCache for Redis is a blazing fast in-memory data store that provides sub-millisecond latency to power internet-scale real-time applications1.

It scales with traffic volume, as ElastiCache for Redis supports horizontal scaling by adding more nodes or shards to the cluster, and vertical scaling by changing the node type2.

It ishighly available, as ElastiCache for Redis supports replication across multiple Availability Zones and automatic failover in case of a primary node failure3.

It preserves user session data even if the user is disconnected and reconnects, as ElastiCache for Redis can store session data, such as user login information and shopping cart contents, in a persistent and durable manner using snapshots or AOF (append-only file) persistence4.

This solution meets the following requirements:

It is cost-effective, as it only uses serverless components that are charged based on usage and do not require any upfront provisioning or maintenance.

It is scalable, as it can handle any number of recipe records that are uploaded to the S3 bucket without any performance degradation or manual intervention.

It is easy to implement, as it does not require any machine learning knowledge or complex data processing logic. Amazon Comprehend is a natural language processing service that can automatically extract entities such as ingredients from text files. The Lambda function can simply invoke the Comprehend API and store the results in the DynamoDB table.

It is reliable, as it can handle non-food records and errors gracefully. Amazon Comprehend can detect the language and domain of the text files and return an appropriate response. The Lambda function can also implement error handling and logging mechanisms to ensure the data quality and integrity.

To satisfy the security requirements, the solutions architect should download AWS-provided root certificates and provide the certificates in all connections to the RDS instance. This will enable SSL/TLS encryption for data in transit between the application and the RDS instance. SSL/TLS encryption provides a layer of security by encrypting data that moves between the client and the server. Amazon RDS creates an SSL certificate and installs the certificate on the DB instance when the instance is provisioned.The application can use the AWS-provided root certificates to verify the identity of the DB instance and establish a secure connection1.

The other options are not correct because they do not enable encryption for data in transit or are not relevant for the use case. Enabling IAM database authentication on the database is not correct because this option only provides a method of authentication, not encryption.IAM database authentication allows users to use AWS Identity and Access Management (IAM) users and roles to access a database, instead of using a database username and password2. Providing self-signed certificates is not correct because this option is not secure or reliable. Self-signed certificates are certificates that are signed by the same entity that issued them, instead of by a trusted certificate authority (CA).Self-signed certificates can be easily forged or compromised, and are not recognized by most browsers and applications3. Taking a snapshot of the RDS instance and restoring it to a new instance with encryption enabled is not correct because this option onlyenables encryption at rest, not encryption in transit.Encryption at rest protects data that is stored on disk, but does not protect data that is moving between the client and the server4.

These options are the most suitable ways to configure the network architecture to provide the lowest possible latency between nodes. Option A enables and configures enhanced networking on each EC2 instance, which is a feature that improves the network performance of the instance by providing higher bandwidth, lower latency, and lower jitter. Enhanced networking uses single root I/O virtualization (SR-IOV) or Elastic Fabric Adapter (EFA) to provide direct access to the network hardware. You can enable and configure enhanced networking by choosing a supported instance type and a compatible operating system, and installing the required drivers. Option C runs the EC2 instances in a cluster placement group, which is a logical grouping of instanceswithin a single Availability Zone that are placed close together on the same underlying hardware. Cluster placement groups provide the lowest network latency and the highest network throughput among the placement group options. You can run the EC2 instances in a cluster placement group by creating a placement group and launching the instances into it.

Option B is not suitable because grouping the EC2 instances in separate accounts does not provide the lowest possible latency between nodes. Separate accounts are used to isolate and organize resources for different purposes, such as security, billing, or compliance. However, they do not affect the network performance or proximity of the instances. Moreover, grouping the EC2 instances in separate accounts would incur additional costs and complexity, and it would require setting up cross-account networking and permissions.

Option D is not suitable because attaching multiple elastic network interfaces to each EC2 instance does not provide the lowest possible latency between nodes. Elastic network interfaces are virtual network interfaces that can be attached to EC2 instances to provide additional network capabilities, such as multiple IP addresses, multiple subnets, or enhanced security. However, they do not affect the network performance or proximity of the instances. Moreover, attaching multiple elastic network interfaces to each EC2 instance would consume additional resources and limit the instance type choices.

Option E is not suitable because using Amazon EBS optimized instance types does not provide the lowest possible latency between nodes. Amazon EBS optimized instance types are instances that provide dedicated bandwidth for Amazon EBS volumes, which are block storage volumes that can be attached to EC2 instances. EBS optimized instance types improve the performance and consistency of the EBS volumes, but they do not affect the network performance or proximity of the instances. Moreover, using EBS optimized instance types would incur additional costs and may not be necessary for the streaming data workload. References:

Enhanced networking on Linux

Placement groups

Elastic network interfaces

Amazon EBS-optimized instances

This solution meets the requirements most cost-effectively because it leverages the serverless and event-driven capabilities of AWS Lambda and Amazon EventBridge. AWS Lambda allows you to run code without provisioning or managing servers, and you pay only for the compute time you consume. Amazon EventBridge is a serverless event bus service that lets you connect your applications with data from various sources and routes that data to targets such as AWS Lambda. By using Amazon EventBridge, you can create a rule that triggers a Lambda function to run the program when reports are requested, and you can also schedule the rule to run during the last week of each month. This way, you can generate reports in the least amount of time and pay only for the resources you use.

The solution that meets the requirements is to develop an on-premises custom identity broker application or process that uses AWS Security Token Service (AWS STS) to get short-lived credentials. This solution allows the company to use its existing LDAP directory service to authenticate its users to the AWS Management Console, without requiring SAML compatibility.The custom identity broker application or process can act as a proxy between the LDAP directory service and AWS STS, and can request temporary security credentials for the users based on their LDAP attributes and roles. The users can then use these credentials to access the AWS Management Console via a sign-in URL generated by the identity broker. This solution also enhances security by using short-lived credentials that expire after a specified duration.

The other solutions do not meet the requirements because they either require SAML compatibility or do not provide access to the AWS Management Console. Enabling AWS IAM Identity Center (AWS Single Sign-On) between AWS and the on-premises LDAP would require the LDAP directory service to support SAML 2.0, which is not the case for this scenario. Creating an IAM policy that uses AWS credentials and integrating the policy into LDAP would not provide access to the AWS Management Console, but only to the AWSAPIs. Setting up a process that rotates the IAM credentials whenever LDAP credentials are updated would also not provide access to the AWS Management Console, but only to the AWS CLI. Therefore, these solutions are not suitable for the given requirements.

To provide access to the SQS queue to the other company without giving up its own account permissions, a solutions architect should create an SQS access policy that provides the other company access to the SQS queue. An SQS access policy is a resource-based policy that defines who can access the queue and what actions they can perform. The policy can specify the AWS account ID of the other company as a principal, and grant permissions for actions such as sqs:ReceiveMessage, sqs:DeleteMessage, and sqs:GetQueueAttributes. This way, the other company can poll the queue using its own credentials, without needing to assume a role or use cross-account access keys. References:

Using identity-based policies (IAM policies) for Amazon SQS

Using custom policies with the Amazon SQS access policy language

This solution meets the requirements because it allows the company to use a single Direct Connect connection to connect to multiple VPCs in different Regions using a Direct Connect gateway. A Direct Connect gateway is a globally available resource that enables you to connect your on-premises network to VPCs in any AWS Region, except the AWS China Regions. You can associate a Direct Connect gateway with a transit gateway or a virtual private gateway in each Region. By routing traffic from the virtual private gateways of the VPCs to the Direct Connect gateway, you can enable inter-Region and on-premises connectivity for your VPCs. This solution is scalable because you can add more VPCs in different Regions to the Direct Connect gateway without creating additional connections. This solution also reduces operational overhead because you do not need to manage multiple VPN appliances, VPN connections, or VPC peering connections.

The most cost-effective solution for the online video game company is to configure a Network Load Balancer with the required protocol and ports for the internet traffic and specify the EC2 instances as the targets. This solution will enable the company to handle millions of UDP requests per second with ultra-low latency and high performance.

A Network Load Balancer is a type of Elastic Load Balancing that operates at the connection level (Layer 4) and routes traffic to targets (EC2 instances, microservices, or containers) within Amazon VPC based on IP protocol data. A Network Load Balancer is ideal for load balancing of both TCP and UDP traffic, as it is capable of handling millions of requests per second while maintaining high throughput at ultra-low latency. A Network Load Balancer also preserves the source IP address of the clients to the back-end applications, which can be useful for logging or security purposes1.

To meet the requirements of the web application in the most secure manner, the company should create a Route 53 Resolver outbound endpoint, create a resolver rule, and associate the resolver rule with the VPC. This solution will allow the application to use private DNS records to communicate with the on-premises services from a VPC. Route 53 Resolver is a service that enables DNS resolution between on-premises networks and AWS VPCs. An outbound endpoint is a set of IP addresses that Resolver uses to forward DNS queries from a VPC to resolvers on an on-premises network. A resolver rule is a rule that specifies the domain names for which Resolver forwards DNS queries to the IP addresses that you specify in the rule. By creating an outbound endpoint and a resolver rule, and associating them with the VPC, the company can securely resolve DNS queries for the on-premises services using private DNS records12.

The other options are not correct because they do not meet the requirements or are not secure. Creating a Route 53 Resolver inbound endpoint, creating a resolver rule, and associating the resolver rule with the VPC is not correct because this solution will allow DNS queries from on-premises networks to access resources in a VPC, not vice versa. An inbound endpoint is a set of IP addresses that Resolver uses to receive DNS queries from resolvers on an on-premises network1. Creating a Route 53 private hosted zone and associating it with the VPC is not correct because this solution will only allow DNS resolution for resources within the VPC or other VPCs that are associated with the same hosted zone. A private hosted zone is a container for DNS records that are only accessible from one or more VPCs3. Creating a Route 53 public hosted zone and creating a record for each service to allow service communication is not correct because this solution will expose the on-premises services to the public internet, which is not secure. A public hosted zone is a container for DNS records that are accessible from anywhere on the internet3.

This option is the most secure and simple way to encrypt the secrets that are stored in Amazon EKS. AWS Key Management Service (AWS KMS) is a service that allows you to create and manage encryption keys that can be used to encrypt your data. Amazon EKS KMS secrets encryption is a feature that enables you to use a KMS key to encrypt the secrets that are stored in the Kubernetes etcd key-value store. This provides an additional layer of protection for your sensitive data, such as passwords, tokens, and keys. You can create a new KMS key or use an existing one, and then enable the Amazon EKS KMS secrets encryption on the Amazon EKS cluster. You can also use IAM policies to control who can access or use the KMS key.

Option A is not correct because using AWS Secrets Manager to manage, rotate, and store all secrets in Amazon EKS is not necessary or efficient. AWS Secrets Manager is a service that helps you securely store, retrieve, and rotate your secrets, such as database credentials, API keys, and passwords. You can use it to manage secrets that are used by your applications or servicesoutside of Amazon EKS, but it is not designed to encrypt the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using AWS Secrets Manager would incur additional costs and complexity, and it would not leverage the native Kubernetes secrets management capabilities.

Option C is not correct because using the Amazon EBS Container Storage Interface (CSI) driver as an add-on does not encrypt the secrets that are stored in Amazon EKS. The Amazon EBS CSI driver is a plugin that allows you to use Amazon EBS volumes as persistent storage for your Kubernetes pods. It is useful for providing durable and scalable storage for your applications, but it does not affect the encryption of the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using the Amazon EBS CSI driver would require additional configuration and resources, and it would not provide the same level of security as using a KMS key.

Option D is not correct because creating a new AWS KMS key with the alias aws/ebs and enabling default Amazon EBS volume encryption for the account does not encrypt the secrets that are stored in Amazon EKS. The alias aws/ebs is a reserved alias that is used by AWS to create a default KMS key for your account. This key is used to encrypt the Amazon EBS volumes that are created in your account, unless you specify a different KMS key. Enabling default Amazon EBS volume encryption for the account is a setting that ensures that all new Amazon EBS volumes are encrypted by default. However, these features do not affect the encryption of the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using the default KMS key or the default encryption setting would not provide the same level of control and security as using a custom KMS key and enabling the Amazon EKS KMS secrets encryption feature. References:

Encrypting secrets used in Amazon EKS

What Is AWS Key Management Service?

What Is AWS Secrets Manager?

Amazon EBS CSI driver

Encryption at rest

AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and AWS storage services. AWS DataSync can transfer data at speeds up to 10 times faster than open-source tools by using a purpose-built network protocol and parallelizing data transfers. AWS DataSync also handles encryption, data integrity verification, and bandwidth optimization. To use AWS DataSync, users need to deploy a DataSync agent on their on-premises servers, which connects to the NFS servers and syncs the data to Amazon S3. Users can schedule periodic or one-time sync tasks and monitor the progress and status of the transfers.

The other options are not correct because they are either not cost-effective or not suitable for the use case. Setting up AWS Glue to copy the data from the on-premises servers to Amazon S3 is not cost-effective because AWS Glue is a serverless data integration service that is mainly used for extract, transform, and load (ETL) operations, not for simple data backup. Setting up an SFTP sync using AWS Transfer for SFTP to sync data from on premises to Amazon S3 is not cost-effective because AWS Transfer for SFTP is a fully managed service that provides secure file transfer using the SFTP protocol, which is more suitable for exchanging data with third parties than for backing up data. Setting up an AWS Direct Connect connection between the on-premises data center and a VPC, and copying the data to Amazon S3 is not cost-effective because AWS Direct Connect is a dedicated network connection between AWS and the on-premises location, which has high upfront costs and requires additional configuration.

This solution meets the requirements because it allows the company to move the application to a fully managed service without managing any servers or storage infrastructure. AWS Fargate is a serverless compute engine for containers that runs the Amazon ECS tasks. With Fargate, the company does not need to provision, configure, or scale clusters of virtual machines to run containers. Amazon EFS is a fully managed file system that can be accessed by multiple containers concurrently. With EFS, the company does not need to provision and manage storage capacity. EFS provides a simple interface to create and configure file systems quickly and easily. The company can use the EFS volume as a persistent storage volume mounted in the containers to store the persistent data. The company can also use the EFS mount helper to simplify the mountingprocess. References: Amazon ECS on AWS Fargate, Using Amazon EFS file systems with Amazon ECS, Amazon EFS mount helper.

The solution that will meet the requirements is to deploy AWS Storage Gateway using cached volumes and use Storage Gateway to store data in Amazon S3 while retaining copies of frequently accessed data subsets locally. This solution will allow the company to migrate its storage infrastructure to AWS while minimizing bandwidth costs, as it will only transfer data that is not cached locally. The solution will also allow for immediate retrieval of data at no additional cost, as the cached volumes will provide low-latencyaccess to the most recently used data. The data stored in Amazon S3 will be durable, scalable, and secure.

The other solutions are not as effective as the first one because they either do not meet the requirements or introduce additional costs or complexity. Deploying Amazon S3 Glacier Vault and enabling expedited retrieval will not meet the requirements, as it will incur additional costs for both storage and retrieval. Amazon S3 Glacier is a low-cost storage service for data archivingand backup, but it has longer retrieval times than Amazon S3. Expedited retrieval is a feature that allows faster access to data, but it charges a higher fee per GB retrieved. Provisioned retrieval capacity is a feature that reserves dedicated capacity for expedited retrievals, but it also charges a monthly fee per provisioned capacity unit. Deploying AWS Storage Gateway using stored volumes to store data locally and use Storage Gateway to asynchronously back up point-in-time snapshots of the data to Amazon S3 will not meet the requirements, as it will not migrate the storage infrastructure to AWS, but only create backups. Stored volumes are volumes that store the primary data locally and back up snapshots to Amazon S3. This solution will not reduce the storage capacity needed on-premises, nor will it leverage the benefits of cloud storage. Deploying AWS Direct Connect to connect with the on-premises data center and configuring AWS Storage Gateway to store data locally and use Storage Gateway to asynchronously back up point-in-time snapshots of the data to Amazon S3 will not meet the requirements, as it will also not migrate the storage infrastructure to AWS, but only create backups. AWS Direct Connect is a service that establishes a dedicated network connection between the on-premises data center and AWS, which can reduce network costs and increase bandwidth. However, this solution will also not reduce the storage capacity needed on-premises, nor will it leverage the benefits of cloud storage.