DVA-C02 AWS Certified Developer - Associate Question and Answers

Amazon Q Developer Pro is an AWS-native, generative AI–powered development assistant designed to help developers write, test, and review code directly within supported IDEs such as Visual Studio Code and JetBrains. AWS documentation highlights Amazon Q Developer Pro as a productivity tool that accelerates development without requiring additional infrastructure .

Amazon Q Developer Pro can automatically generate unit tests , suggest improvements, identify potential bugs, and provide context-aware code reviews . Because it integrates directly into the IDE, developers receive immediate feedback during development, which shortens feedback loops and improves overall code quality.

Options A, B, and C require additional tooling, scripting, or manual processes. While CodeBuild and CodeGuru Reviewer are valuable services, they introduce pipeline complexity and do not provide real-time, IDE-integrated assistance for writing tests and reviewing code.

Amazon Q Developer Pro uniquely satisfies all stated requirements:

No infrastructure to manage

Native IDE integration

Automated unit test generation

AI-assisted code reviews

Therefore, using Amazon Q Developer Pro is the most efficient and modern AWS-recommended solution.

To prove integrity (and authenticity) of data in transit, the correct cryptographic primitive is a digital signature. AWS KMS supports signing and verification operations using asymmetric KMS keys designed for signing (for example, RSA_SIGN_PSS, RSA_SIGN_PKCS1, or ECC_NIST_P256 key specs). The developer signs the data (or more commonly, a hash of the data) using the private key, and the external recipient verifies the signature using the corresponding public key. If the data is modified in transit, signature verification fails, proving the content was changed.

Option C exactly describes this model: sign with the private key, share the public key. The private key remains protected (ideally never leaving KMS). The public key can be distributed safely because it cannot be used to forge signatures.

Option A (encryption) provides confidentiality, not integrity proof to a third party, and sharing a symmetric encryption key is insecure and breaks key management principles.

Option B is incorrect because symmetric keys do not provide non-repudiation and generally require the verifier to possess the same secret key, which would allow the verifier to forge signatures too. While HMAC can validate integrity between trusted parties, it does not meet the typical “prove integrity” requirement to an external party without sharing a secret.

Option D is backward and insecure: you never share a private key.

Therefore, use an asymmetric KMS signing key to sign with the private key and provide the public key for verification.

Lambda Layers allow you to pull common code and dependencies out of your function ' s deployment package. By putting the open source modules into a layer shared by all three functions, you only need to update the layer once when a bug is found. Then, you update the functions to use the new layer version. This is the most efficient way to manage shared dependencies across multiple functions.

This solution allows the developer to troubleshoot the failure by capturing unprocessed events in a queue for further analysis. Dead Letter Queues (DLQs) are queues that store messages that could not be processed by a service, such as Lambda, for various reasons, such as configuration errors, throttling limits, or permissions issues. The developer can configure DLQs for Lambda functions by sending events to either an Amazon Simple Queue Service (SQS) queue or an Amazon Simple Notification Service (SNS) topic. The developer can then inspect the messages in the queue or topic to identify and fix the root cause of the failure. Configuring AWS CloudTrail logging will not capture invocation failures for asynchronous Lambda invocations, but only record API calls made by or on behalf of Lambda. Configuring Amazon Simple Workflow Service (SWF) or AWS Config will not process any direct unprocessed events, but require additional integration and configuration.

To meet the requirement for encryption " at rest, " you must enable Server-Side Encryption (SSE) on the SNS topic (Option A). To ensure encryption " in transit, " you must enforce HTTPS. The aws:SecureTransport global condition key checks if the request was made over SSL/TLS. Adding a Deny statement where aws:SecureTransport is false (Option E) effectively mandates that only encrypted connections are allowed to publish to the topic.

Why Option C is Correct:Amazon Verified Permissions provides fine-grained access control capabilities, making it ideal for managing complex user permissions. It integrates with OIDC IdPs for authentication and allows applications to request authorization decisions dynamically. It is also easily adaptable to newer applications.

Why Other Options are Incorrect:

Option A: Cognito identity pools do not natively support fine-grained permission analysis or management.

Option B: Managing permissions in application logic adds significant operational overhead.

Option D: IAM roles are not designed for application-specific fine-grained access control and are more suitable for resource-level permissions.

AWS Documentation References:

Amazon Verified Permissions

The correct answer is B because Amazon CloudWatch Logs Insights is the AWS-native tool for interactively searching, filtering, and analyzing log data that is already stored in CloudWatch Logs. In this case, the developer needs to investigate intermittent Lambda failures within a specific 1-hour period , identify logs related to failures, and capture the timestamps of those failures. Logs Insights is purpose-built for exactly this kind of operational troubleshooting.

AWS documentation describes Logs Insights as a fully integrated log analytics capability that can query log groups by time range and pattern. The developer can run a query that filters for error-related log messages, extracts timestamp fields, and sorts the results to find exactly when failures occurred. This is highly efficient because it does not require exporting logs, writing custom parsing scripts, or manually browsing through streams.

Option A is possible but not the most operationally efficient. Browsing logs through the CLI is slower and more manual, especially for intermittent failures. Option C is inefficient because downloading logs to a local machine adds unnecessary steps and operational overhead. Option D can work for large-scale analytics, but exporting to S3 and querying with Athena is more complex than needed for logs that are already in CloudWatch Logs.

Therefore, the best solution is to use CloudWatch Logs Insights to query the Lambda log group for failure patterns and timestamps. This provides fast, targeted, and operationally efficient troubleshooting directly within AWS.

The issue is write throttling during peak login surges. In DynamoDB provisioned capacity mode, throttling happens when the workload exceeds the table’s configured write capacity units (WCU) (or a partition becomes “hot”). To resolve throttling and reduce latency, the table must have sufficient write capacity available when the surge occurs.

Option B directly addresses this by increasing the table’s provisioned throughput (WCU). With more write capacity, DynamoDB can accept more writes per second without returning throttling errors, which reduces retries and improves end-user latency during peak usage.

Option E is also effective: switching the table to on-demand capacity mode automatically accommodates traffic spikes without the team needing to forecast and pre-provision capacity. On-demand is designed for unpredictable workloads and can scale to handle sudden surges, reducing the risk of throttling caused by under-provisioning. For many teams, this is the fastest operational fix because it removes manual capacity planning and reactive scaling steps.

Why the other options are not the best fit:

A (DAX) improves read performance and reduces read latency, but it does not fix write throttling .

C (retries and exponential backoff) is a best practice for handling throttling gracefully, but it does not “resolve” throttling; it reduces contention and improves success rates at the cost of higher latency, and it does not increase available capacity.

D focuses on control plane operations (schema/index/table management) which are unrelated to data plane write throttling during login surges.

Therefore, the two solutions that meet the requirements to resolve write throttling and reduce latency are B (increase provisioned throughput) and E (switch to on-demand capacity mode for automatic scaling).

The correct answer is C because AWS AppConfig provides built-in support for feature flags , allowing developers to enable or disable features dynamically without redeploying application code. This is exactly what the question requires: keeping features hidden from end users until the features are ready for release, and then selectively activating them when appropriate.

AWS documentation describes AppConfig as a service for managing application configuration in a safe and controlled way. Feature flags in AppConfig let teams separate deployment from release , which is a core best practice in modern application development. A developer can deploy code that contains unfinished or hidden functionality, while the actual visibility and activation of that functionality is controlled through feature flag values stored in an AppConfig configuration profile. When the feature is ready, the flag can be turned on without changing the application package.

Option A is incorrect because AWS AppSync is a managed GraphQL service and is not the AWS feature flag service for application configuration management. Option B is incorrect because DynamoDB Streams are used for capturing item-level changes and reacting to table updates, not for controlled feature activation. Option D is incorrect because Amplify DataStore is intended for client-side data synchronization and offline access patterns, not centralized production feature flag management.

Using AppConfig also provides operational benefits such as deployment strategies, configuration validation, and rollback support, which make it safer than building a custom feature toggle mechanism. For these reasons, AWS AppConfig feature flags are the most appropriate and AWS-recommended solution for hiding prerelease features and selectively enabling them later.

Therefore, C is the correct answer.

AWS Secrets Manager supports multi-Region secret replication, which is designed specifically for redundancy, disaster recovery, and multi-Region applications. With this feature, the primary secret resides in one Region (here, us-west-1) and Secrets Manager automatically maintains a replica in another Region (us-east-1). This provides local read access and resilience if one Region is impaired.

Option A accurately describes the standard configuration: enable secret replication and add us-east-1 as the replica Region. Because encryption keys are Region-scoped, the replica secret in us-east-1 should be encrypted with a KMS key in us-east-1 (either the default Secrets Manager key for that Region or a customer managed key), satisfying encryption requirements and proper key locality.

Option B is incorrect because you don’t configure replication “from the destination.” Replication is configured on the primary secret, and the replica uses a KMS key in the replica Region, not in the source Region.

Option C is not how Secrets Manager replication works. Replication is not only during rotation; it maintains replicas continuously. The “replication rule during rotation” framing is not the standard mechanism.

Option D is inappropriate and insecure/operationally complex: exporting secrets to S3 for replication is not the recommended pattern and introduces unnecessary exposure.

Therefore, enable Secrets Manager multi-Region replication and encrypt replicas with a KMS key in the destination Region.

The correct answer is C because the requirement is for a live feed of log events in near real time , filtered to show only lines that begin with “ERROR.” The live tail feature for AWS Lambda and CloudWatch Logs is specifically intended for this kind of operational debugging. It allows developers to watch logs as they are generated and apply filtering so they can focus only on relevant messages during active troubleshooting.

This is particularly suitable because the function is already in production , and the company wants immediate visibility into timeout-related issues without waiting for a full batch of logs to accumulate. With live tail and a filter expression for ERROR , the developer can observe only the important log lines as new invocations happen, which speeds up root-cause analysis.

Option A is not the best answer because CloudWatch Logs Insights is powerful for querying logs, but it is primarily used for searching and analyzing logs after they have been collected, not as a continuous near-real-time live stream. Option B is incorrect because a subscription filter is used to forward logs to another destination such as Lambda, Kinesis, or Firehose; it is not the simplest way to give an operator a live interactive filtered view. A metric filter also creates metrics from log patterns but does not present the actual live log lines for review. Option D is incorrect because Amazon Athena is not used directly for querying CloudWatch Logs in real-time operational troubleshooting.

Therefore, the best solution is to use live tail with an ERROR filter to inspect relevant Lambda log events as they occur.

Requirement Summary:

E-commerce app using Amazon RDS for MySQL

Needs caching layer for most-viewed products

Evaluate Options:

A. Add a cache node to RDS

No such feature in RDS for MySQL

Caching must be implemented outside RDS

B. ElastiCache for Redis (OSS)

Purpose-built for caching frequently accessed data

Reduces read pressure on RDS

Fast in-memory access (microseconds)

Seamless integration into app logic

C. DynamoDB DAX

DAX is for accelerating DynamoDB, not RDS

D. RDS standby instance

Read from standby is not allowed

Standby is for failover only, not for load balancing

ElastiCache for Redis: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/WhatIs.html

Caching with Redis for RDS: https://aws.amazon.com/blogs/database/caching-strategies-using-amazon-elasticache-for-read-heavy-workloads-on-amazon-rds/

Amazon SNS is the most efficient fanout service for this requirement. A single publish operation can distribute the same message payload to multiple subscribers, including Lambda functions. Because the payload is 100 KB, it is within the SNS message size limit, making direct fanout practical. This centralizes the event origin in the parent account and avoids custom orchestration code. Using STS AssumeRole and manually invoking every Lambda function creates unnecessary code, permissions management, and retry complexity. SQS is a queueing service where messages are consumed, not broadcast to all consumers; multiple Lambda functions polling the same queue would compete for messages rather than each receive every payload. An EC2 worker is the worst option because it adds server management and polling overhead. SNS topic subscriptions are the intended pub/sub design.

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The requirement has two parts: (1) authenticate users with a third-party SAML IdP , and (2) after authentication, allow those users to access AWS services like Amazon S3 and DynamoDB. The AWS pattern for this is to federate the external identity into AWS and then exchange that identity for temporary AWS credentials .

Amazon Cognito identity pools are designed to provide AWS credentials to authenticated users (via AWS STS) so the users can call AWS services directly or through an application backend. Identity pools support federation with external identity providers, including SAML 2.0 . When a user authenticates with the third-party SAML IdP, the identity pool can accept the SAML assertion, map the user to an IAM role, and return temporary, scoped credentials (AccessKeyId/SecretAccessKey/SessionToken) associated with that role. Those credentials can be restricted using IAM policies to only the required S3 buckets, DynamoDB tables, and actions, satisfying least privilege.

Option A is incorrect because a Cognito user pool is primarily a user directory and OIDC/OAuth provider for application authentication; while user pools can integrate with SAML IdPs for federation, user pools alone do not directly issue AWS service credentials. The key requirement here is access to S3/DynamoDB, which is the role of an identity pool . Option C is not appropriate because IAM is not intended to provide end-user sign-up/sign-in for external users; it is for AWS identities and permissions. Option D is unrelated: CloudFront signed URLs control access to CloudFront-distributed content and do not authenticate users with SAML or provide AWS credentials.

Therefore, B meets both requirements: federate SAML authentication through a Cognito identity pool and provide temporary AWS credentials for accessing S3 and DynamoDB.

This solution will meet the requirements by creating a single S3 bucket policy that denies access to the S3 bucket unless the request comes from one of the specified VPC endpoints. The aws:SourceVpce condition key is used to match the ID of the VPC endpoint that is used to access the S3 bucket. The StringNotEquals condition operator is used to negate the condition, so that only requests from the listed VPC endpoints are allowed. Option A is not optimal because it will create multiple S3 bucket policies, which is not possible as only one bucket policy can be attached to an S3 bucket. Option B is not optimal because it will use the aws:SourceVpc condition key, which matches the ID of the VPC that is used to access the S3 bucket, not the VPC endpoint. Option C is not optimal because it will use the StringNotEquals condition operator with a single value, which will deny access to the S3 bucket from all VPC endpoints except one.

Amazon API Gateway Stages (staging, testing, production) are the standard way to handle different environments for a single REST API. Each stage is a reference to a specific deployment snapshot of the API. This provides a built-in promotion path: once a deployment is tested in the " test " stage, it can be deployed to the " production " stage. This is the most operationally efficient method as it uses the native features of the service.

AWS best practice is to store sensitive values like database passwords, OAuth tokens, and API keys in a dedicated secrets service rather than in source code or configuration files. AWS Secrets Manager is specifically designed for this use case: secure storage, retrieval through APIs/SDKs, fine-grained IAM access control, encryption at rest, auditing through CloudTrail, and—critically—built-in secret rotation.

AWS documentation describes Secrets Manager rotation as an automated process that uses a rotation schedule and typically invokes a Lambda function to update the secret in both Secrets Manager and the target system (for example, a database). The rotation schedule can be configured to a custom interval, including every 6 months, satisfying the requirement.

Option A is incorrect because AWS KMS manages encryption keys, not application secrets. KMS key rotation rotates KMS keys, not database credentials or API tokens.

Option B is incorrect because ACM manages TLS certificates, not arbitrary secrets like API keys and OAuth tokens.

Option C is incorrect because EventBridge can schedule events, but it is not a secrets storage service and does not provide secure secret lifecycle management and integrated rotation workflows by itself.

Therefore, storing secrets in AWS Secrets Manager and enabling automatic rotation with a 6-month schedule is the correct solution.

AWS Serverless Application Model (AWS SAM) is an open-source framework that enables developers to build and deploy serverless applications on AWS. AWS SAM uses a template specification that extends AWS CloudFormation to simplify the definition of serverless resources such as API Gateway, DynamoDB, and Lambda. The developer can use AWS SAM to define serverless resources in YAML and deploy them using the AWS SAM CLI.

To meet the requirement that developers cannot view credit card numbers while the fraud detection account can , the solution must (1) ensure the sensitive data is masked at ingestion/storage in CloudWatch Logs, and (2) allow only an authorized principal to view unmasked values .

First, the developers must attach the data protection policy to the CloudWatch log group that receives the API Gateway HTTP API access logs (and/or the Lambda log group if credit card numbers can appear there). A CloudWatch Logs data protection policy is applied at the log group level and instructs CloudWatch Logs to identify sensitive data using managed data identifiers (for example, CreditCardNumber ) and then apply the configured de-identification action (here, masking). Without attaching the policy to the log group, the masking/redaction behavior will not be enforced for newly ingested log events, and developers could still see raw request parameters.

Second, to permit only the fraud detection account to reveal the masked values when necessary, the IAM role that the fraud detection account assumes must have the specific CloudWatch Logs permission to unmask protected data. Granting logs:Unmask to that role enables authorized access to view the sensitive values while keeping them masked for everyone else who lacks that permission (such as developer accounts assuming other roles).

Options A and E are not required for CloudWatch Logs data protection enforcement; the core controls are log-group policy attachment (to perform masking) and IAM authorization (to permit unmasking only for the fraud role). Therefore, the correct combination is C (apply the policy to the log group that captures the HTTP API logs) and B (grant logs:Unmask to the fraud detection role).

HTTP 429 errors indicate throttling (“Too Many Requests”). In this architecture, throttling can occur at multiple layers (API Gateway rate limits, downstream AWS service API throttles, or dependency throttling). Regardless of which service is throttling, the correct resilience pattern is to implement retries with exponential backoff and jitter for retryable failures. AWS SDKs and AWS best practices recommend backoff to reduce contention and allow the system to recover, while ensuring requests eventually succeed.

Option C directly addresses the problem and the requirement “ensure that all of the application ingests all files.” With a retry + backoff pattern, transient throttling is handled gracefully: failed operations are retried after increasing delays, avoiding immediate retry storms that worsen throttling. This increases successful completion rate without requiring major architectural changes.

Option A (Transfer Acceleration) can improve upload latency from geographically distributed clients, but it does not resolve request throttling (429) caused by API limits.

Option B (multipart upload) helps with large object uploads and can improve throughput/reliability for big files, but it does not inherently prevent 429 throttling at API Gateway or other throttled calls.

Option D (Intelligent-Tiering) affects storage cost optimization, not ingestion throttling.

Comprehensive and Detailed Step-by-Step Explanation:

To ensure successful software deployments using AWS CodeDeploy, the application specification file (appspec.yml or appspec.json) must adhere to specific requirements:

File Format Requirement (Option B):

The appspec.yml file is a YAML-formatted file required for defining deployment actions and file locations. This is the modern and recommended format for application specification files.

It can also be a JSON-formatted file named appspec.json, but YAML is most commonly used and accepted.

File Placement Requirement (Option D):

The application specification file must reside in the root directory of the application ' s source code. This is necessary so that AWS CodeDeploy can detect and use the file during deployment.

Incorrect Options:

Option A: While a JSON-formatted file (appspec.json) is valid, this is not a mandatory requirement. YAML is also acceptable, and this option does not account for it.

Option C: The application specification file is not required to be stored in AWS CodeBuild; it must be included in the source code ' s directory structure.

Option E: The application specification file does not need to be stored in Amazon S3. S3 is commonly used for application artifacts, but the appspec.yml or appspec.json file must exist within the deployment package or source code root.

The correct answer is B because when an IAM role is attached to an Amazon EC2 instance through an instance profile, changes to the permissions policy for that role propagate automatically to applications that use the instance role credentials. This is the least disruptive approach because the developer does not need to restart, hibernate, terminate, or replace the EC2 instance.

AWS documentation explains that applications running on EC2 can obtain temporary security credentials from the instance metadata service for the attached IAM role. When the IAM policy attached to that role is updated to allow Amazon S3 read access , new temporary credentials issued for the role will reflect the updated permissions. This makes it possible to correct the access issue without changing the application deployment model or interrupting the running instance.

Option A is unnecessary because terminating and relaunching the instance causes avoidable disruption. Option C is also unnecessary because hibernation and restart are not required for IAM policy changes to take effect. Option D is not the best answer because although bucket policies can grant access, the issue identified is specifically that the instance role lacks S3 read permission , and restarting the instance is still unnecessary.

The AWS best-practice approach is to grant the required permission directly to the IAM role used by the EC2 instance. This keeps authorization centralized and lets the running application continue using temporary role credentials with minimal operational impact.

Therefore, the solution with the least application disruption is to add the S3 read permission to the IAM role , making B the correct answer.

The Deployment Preference Type property specifies how traffic should be shifted between versions of a Lambda function1. The Canary10Percent10Minutes option means that 10% of the traffic is immediately shifted to the new version, and after 10 minutes, the remaining 90% of the traffic is shifted1. This matches the requirement of shifting 10% of the traffic for the first 10 minutes, and then switching all traffic to the new version.

The AutoPublishAlias property enables AWS SAM to automatically create and update a Lambda alias that points to the latest version of the function1. This is required to use the Deployment Preference Type property1. The alias name can be specified by the developer, and it can be used to invoke the function with the latest code.

Amazon S3 Lifecycle rules are specifically designed to manage object versions over time. When versioning is enabled, lifecycle rules can be configured to expire noncurrent versions , retaining only a defined number of recent versions.

AWS documentation states that lifecycle rules can be used to “retain a specific number of newer noncurrent versions.” By configuring a lifecycle rule to keep one noncurrent version , Amazon S3 automatically deletes older versions while retaining the current and immediately previous versions.

Bucket policies (Option A) control access, not retention. S3 Object Lock (Option C) is designed for regulatory compliance and write-once-read-many (WORM) use cases and is not suitable for routine version cleanup. Suspending versioning (Option D) does not remove existing versions and increases application complexity.

Therefore, an S3 Lifecycle rule is the correct and AWS-recommended solution.

Requirement Summary:

Prevent unauthorized code changes in AWS Lambda

Ensure only trusted code is deployed

AWS Lambda supports Code Signing:

You can configure code signing in Lambda using AWS Signer

Packages must be digitally signed and verified against the signing profile

Rejects unauthorized/modified packages automatically

Evaluate Options:

A. Trusted code option in CodeDeploy

No such feature exists for Lambda

CodeDeploy is more for EC2/On-Prem/Containers, not Lambda code signing

B. Define code signing config + use AWS Signer

This is exactly how AWS enforces trusted code deployment

Attach a code signing configuration to the Lambda function

Use AWS Signer to digitally sign deployment packages

C. Link to KMS to sign code

KMS is not used to sign Lambda packages

KMS is for data encryption, not application code integrity

D. Set KmsKeyArn

This configures data encryption, not code signing

Lambda code signing: https://docs.aws.amazon.com/lambda/latest/dg/configuration-codesigning.html

AWS Signer overview: https://docs.aws.amazon.com/signer/latest/developerguide/what-is-signer.html

To provide login functionality with branded sign-in pages and minimal custom code, the best solution is Amazon Cognito Hosted UI. Cognito User Pools include a managed authentication service that can handle sign-up/sign-in, MFA options, password resets, and OAuth/OpenID Connect flows. The Hosted UI provides ready-to-use authentication pages and endpoints, which removes the need to build and maintain custom authentication pages and backend logic.

AWS allows customization of the Hosted UI to match company branding (for example, logo, colors, and CSS styling). This directly meets the requirement that “all pages must match branding” while minimizing development effort. The application can integrate with Cognito using standard authorization flows and tokens, which works well with Lambda-based architectures (for example, API Gateway authorizers with Cognito user pools).

Option A (S3 static pages) would still require implementing authentication flows, session handling, token management, and secure integration with the backend—significantly more custom code.

Option B requires writing and maintaining custom sign-in page serving logic plus authentication handlers, which increases code and operational overhead.

Option C (Lambda@Edge) is even more complex, introducing edge execution and additional deployment/versioning considerations, and still requires custom auth logic.

Therefore, using Amazon Cognito User Pools with the Hosted UI and branding customization is the lowest-code, most maintainable solution.

The requirement is to test a new Lambda version without impacting the production API, and to do so with the least operational overhead. In API Gateway (REST API), the simplest pattern is to create a separate stage (for example, test) and use a stage variable to control which Lambda function version (or alias) the integration invokes.

With option B, the developer creates a new stage and defines a stage variable (commonly something like lambdaAlias or lambdaVersion). The API method integration URI can reference this stage variable so that each stage points to a different Lambda alias/version. The production stage (for example, prod) continues to call the stable version, while the test stage calls the new version. This cleanly isolates testing traffic and prevents any effect on the production REST API because stages have distinct invoke URLs and deployments.

This approach is operationally efficient because it avoids duplicating the API definition (new REST API) and avoids creating extra resources/methods that must be maintained. It also provides a repeatable process: future versions can be tested by updating only stage variables or alias routing rather than restructuring the API.

Why not the others:

A adds additional resources/methods and increases API surface area and maintenance.

C duplicates the entire API, increasing overhead and configuration drift risk.

D would impact production because it changes the integration used by the existing method.

Therefore, using a new stage + stage variables to route to the updated Lambda version meets the requirements with minimal overhead.

https://docs.aws.amazon.com/cognito/latest/developerguide/amazon-cognito-integrating-user-pools-with-identity-pools.html

Drafts are represented by noncurrent object versions after the final version is written. They are frequently accessed for the first month, so they should remain in a frequent-access storage class during that period. After 30 days, access is rare but the drafts must still be available within minutes. S3 Glacier Instant Retrieval is designed for rarely accessed archive data that still needs millisecond retrieval, making it a better fit than Deep Archive, which is not suitable for minute-level access. The drafts must be deleted after more than 1 year, so a noncurrent version expiration action is required after that period. AWS S3 Lifecycle supports transitions for noncurrent versions and expiration of noncurrent object versions. ( AWS Documentation )

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Lambda throttling occurs when the number of concurrent executions exceeds the available concurrency. This can happen due to account-level concurrency limits, function-level reserved concurrency limits, or sudden traffic spikes.

The most operationally efficient ways to reduce throttling are to increase available concurrency:

Option C: Increasing the function’s reserved concurrency can reduce throttling when the function is being constrained by a too-low reserved concurrency value. Reserved concurrency guarantees a fixed amount of concurrency for the function (and also caps it). If the cap is currently too low, raising it allows more parallel executions and reduces throttles.

Option E: If throttling is due to the account concurrency quota being reached (or burst scaling limits in some patterns), the correct fix is to request a service quota increase. This increases the total available concurrency capacity for the account (and/or specific dimensions), allowing more simultaneous executions across functions.

Why the others are not correct:

A (migrate to EKS) is high effort and not operationally efficient for solving Lambda throttling.

B (maximum age of events) applies to asynchronous event retries/queues; it does not reduce throttling—it only changes how long events remain eligible for processing.

D is irrelevant: adding lambda:GetFunctionConcurrency to the execution role only allows reading settings and does not change throttling behavior.

Therefore, the best operational fixes are to increase reserved concurrency and request a concurrency quota increase.

Amazon API Gateway supports multiple stages , allowing developers to deploy and test different versions of an API independently. By creating a new test stage , the developer can route traffic to an updated Lambda function without impacting production users.

AWS documentation recommends using stage variables to dynamically reference different Lambda function versions or aliases. This approach enables safe testing while reusing the same API configuration.

Canary deployments (Option A) expose a portion of production users to the new code, which violates the requirement. Changing the endpoint type (Option B) disrupts production. Duplicating the entire stack (Option D) introduces unnecessary complexity and overhead.

Using a separate test stage is the most efficient, low-risk, and AWS-recommended solution.

Amazon Cognito User Pools: A managed user directory service, simplifying user registration and login.

Social Identity Providers: Cognito supports integration with external providers (e.g., Google, Facebook), reducing development effort.

IAM Roles for Authorization: Cognito-managed IAM roles grant fine-grained access to AWS resources (like Lambda functions).

Operational Overhead: Cognito minimizes the need to manage user identities and credentials independently.

SNS as a Fan-out Mechanism: SNS is perfect for triggering multiple actions from a single event (here, the image upload).

Workflow:

SNS Topic: Create an SNS topic that will be the central notification point.

S3 Event Notification: Configure the S3 bucket to send ' Object Created ' event notifications to the SNS topic.

Lambda Subscription: Subscribe your thumbnail-creating Lambda function to the SNS topic.

Email Subscription: Subscribe an email address to the SNS topic to trigger notifications.

The IAM policy shown in the image is a resource-based policy that grants or denies access to an S3 bucket based on certain conditions. The first statement allows access to any S3 action on any object in the “DOC-EXAMPLE-BUCKET” bucket when the request is made over HTTPS (the value of aws:SecureTransport is true). The second statement denies access to the s3:GetObject and s3:PutObject actions on any object in the “DOC-EXAMPLE-BUCKET/secrets” prefix when the request is made over HTTP (the value of aws:SecureTransport is false). Therefore, the policy allows access on all objects in the “DOC-EXAMPLE-BUCKET” bucket except on objects that start with “secrets”. Reference: Using IAM policies for Amazon S3

This solution will ensure that the repository package’s unit tests run in the new deployment environment with the least overhead because it uses AWS CodeBuild to build and test the code in a fully managed service, and AWS CodePipeline to orchestrate the deployment stages and actions. Option A is not optimal because it will use AWS CodeCommit instead of AWS CodeBuild, which is a source control service, not a build and test service. Option C is not optimal because it will use AWS CodeDeploy instead of AWS CodeBuild, which is a deployment service, not a build and test service. Option D is not optimal because it will add an action to the source stage instead of creating a new stage, which will not follow the best practice of separating different deployment phases.

S3Bucket and S3Key: These properties in a CloudFormation AWS::Lambda::Function resource specify the location of the function ' s code in S3.

Least Development Effort: This solution minimizes code changes, relying on CloudFormation to reference the existing S3 deployment package.

The response from the API call indicates that the ProvisionedThroughputExceededException exception has occurred. This exception means that the rate of incoming requests exceeds the throughput limit for one or more shards in a stream. To mitigate this exception, the developer can use one or more of the following techniques:

Implement retries with exponential backoff. This will introduce randomness in the retry intervals and avoid overwhelming the shards with retries.

Reduce the frequency and/or size of the requests. This will reduce the load on the shards and avoid throttling errors.

Increase the number of shards in the stream. This will increase the throughput capacity of the stream and accommodate higher request rates.

Use a PutRecord API instead of PutRecords. This will reduce the number of records per request and avoid exceeding the payload limit.

The requirements are: (1) store an API key that a Lambda function will use, (2) ensure the secret is encrypted at rest using AWS KMS , and (3) the company must control key rotation , which implies using a customer managed KMS key (CMK) rather than an AWS managed key.

Option C meets the requirements by storing the API key in AWS Systems Manager Parameter Store as a SecureString parameter encrypted with a customer managed KMS key . SecureString is designed for sensitive configuration data and integrates with KMS so the organization can choose the CMK, manage its lifecycle, and control rotation policies. The Lambda function can retrieve the parameter at runtime using the AWS SDK, and IAM policies can tightly control access to the parameter and to the KMS key.

Option E also meets the requirements by storing the API key in a Lambda environment variable encrypted with a customer managed KMS key . Lambda encrypts environment variables at rest and allows you to specify a customer managed KMS key for encryption. This gives the company control over key rotation and key policy, satisfying the “must control key rotation” requirement. The function reads the value from the environment at runtime without additional network calls.

Why the other options fail:

A uses an AWS managed KMS key, which does not satisfy the requirement for the company to control rotation (you cannot manage rotation of AWS managed keys in the same way).

B is a plain String parameter, which is not encrypted as a secret and does not meet the at-rest encryption requirement.

D uses an AWS managed KMS key, again failing the company-controlled rotation requirement.

Therefore, the two valid solutions are C (Parameter Store SecureString with a customer managed CMK) and E (Lambda environment variable encrypted with a customer managed CMK).

The correct answer is A because AWS Lambda requires permissions in the function’s execution role to create log groups, create log streams, and put log events into Amazon CloudWatch Logs . If the Lambda function is throwing errors but there are no CloudWatch log entries, the most likely cause is that the execution role does not include the necessary logging permissions. AWS documentation states that Lambda automatically sends logs from function code and the execution environment to CloudWatch Logs, but this behavior depends on the function’s IAM role having permissions such as logs:CreateLogGroup , logs:CreateLogStream , and logs:PutLogEvents .

This aligns with the scenario because the source code already attempts to write logs before saving data to DynamoDB. If the function is invoked and errors are visible in CloudWatch metrics, but logs never appear, then logging is failing before or during delivery to CloudWatch Logs. Assigning the proper IAM permissions resolves that problem directly.

Option B is incorrect because CloudWatch Lambda Insights provides enhanced monitoring and performance telemetry, but it does not replace the basic permissions required for CloudWatch Logs delivery. Option C is incorrect because AWS X-Ray helps trace requests and diagnose latency or service interactions, but it does not solve missing log stream permissions. Option D is incorrect because CloudWatch does not need to be added as a trusted principal to the Lambda execution role; the trusted entity for that role is the Lambda service .

Therefore, the developer should update the Lambda execution role to include the required CloudWatch Logs permissions.

An Application Load Balancer can invoke Lambda functions as targets, but ALB must be explicitly granted permission to invoke the function. This is done by adding a resource-based permission to the Lambda function policy that allows the Elastic Load Balancing service principal to call lambda:InvokeFunction, typically scoped to the specific target group ARN.

If the developer registers the function as a target but does not add the required permission, the ALB will not be able to invoke the function when requests arrive. This is a common integration oversight: the target registration succeeds, but invocation fails because Lambda denies the call.

Option A is incorrect because ALB does support Lambda targets.

Option B is incorrect because Lambda targets can be configured through the console, CLI, or APIs.

Option D is unrelated: cross-zone load balancing affects distribution of traffic across AZs for instance/IP targets, not whether Lambda invocation is authorized.

AWS Lambda layers are specifically designed to share common code and dependencies across multiple Lambda functions. A layer is a ZIP archive that contains libraries, a custom runtime, or other function dependencies. Layers are versioned, so the developer can publish a new layer version when libraries change and then update functions to reference the new version. This directly meets the requirements to centralize libraries, update conveniently, and keep libraries versioned—while requiring minimal development work.

Using a single shared layer significantly reduces duplication across function deployment packages. It also simplifies CI/CD because the developer can build and publish the layer independently and reuse it across many functions.

Option A (CodeArtifact) is useful for dependency management, but it still requires each Lambda deployment to package dependencies or download them during build. It’s more moving parts than a layer for this use case.

Option B (container images) can centralize dependencies but typically requires more effort: building, scanning, versioning container images, and updating function image URIs. It’s heavier operationally than layers for “shared custom libraries.”

Option D (EFS) can be mounted by Lambda, but introduces networking considerations (VPC config), EFS lifecycle/permissions, and is unnecessary for simply sharing libraries. Also, cold start and operational overhead can increase.

Therefore, creating a Lambda layer for the shared libraries is the least-effort, most standard approach.

Comprehensive and Detailed Step-by-Step Explanation:

Issue Analysis:

The stream uses service name as the partition key. This can cause " hot partition " issues when a few service names generate significantly more logs compared to others, causing uneven distribution of data across shards.

Metrics show that the write capacity used is below provisioned capacity, which confirms that the throughput errors are due to shard-level limits and not overall capacity.

Option C: Change Partition Key to Creation Timestamp:

By changing the partition key to the creation timestamp (or a composite key including timestamp), the distribution of data across shards can be randomized, ensuring an even spread of records.

This resolves the shard overutilization issue and eliminates ProvisionedThroughputExceededException.

Why Other Options Are Incorrect:

Option A: Switching to on-demand capacity mode might temporarily alleviate the issue, but the root cause (hot partitioning) remains unresolved.

Option B: Adding shards increases capacity but does not fix the skewed data distribution caused by using the service name as the partition key.

Option D: Creating separate streams for each service adds unnecessary complexity and does not scale well as the number of services grows.

Amazon SQS standard queues provide at-least-once delivery, which means messages can be delivered more than once. When Lambda is triggered by an SQS standard queue, duplicate delivery can occur due to retries, visibility timeouts, or transient errors. Therefore, “processed multiple times” is expected behavior unless the system implements deduplication or idempotency.

The most cost-effective way within the provided options to reduce duplicate processing is to use an SQS FIFO queue with deduplication. FIFO queues support exactly-once processing semantics within the constraints of the service (by preventing duplicate message delivery within the deduplication interval when a MessageDeduplicationId is used or content-based deduplication is enabled). This directly mitigates duplicate processing without requiring large architectural changes.

Option B (DLQ) is important for handling poison messages, but it does not prevent duplicates in normal processing; it only captures messages that fail repeatedly.

Option C (concurrency = 1) reduces parallelism but does not eliminate duplicates; the same message can still be delivered again if the visibility timeout expires or retries occur.

Option D is a major redesign and not cost-effective for simply addressing duplicate SQS message processing.

💡 Note: In real production systems, the best practice is to make processing idempotent (so duplicates do no harm). But among the choices, FIFO + deduplication is the most direct and cost-effective fix.

Therefore, switch to an SQS FIFO queue and use deduplication.

The correct answer is B because Amazon EventBridge is designed for building loosely coupled event-driven architectures . In this scenario, when an order is placed, the order processing system can publish a single event to an EventBridge event bus. Then, multiple independent consumers can react to that event through separate rules and targets . This matches the requirement to update inventory, send email, notify shipping, and update order history without tightly coupling those actions together.

EventBridge is especially well suited when the company wants to add new processing steps later . A new consumer can be added simply by creating another rule and target for the existing order event, without changing the original order submission code or the existing consumers. This supports extensibility and separation of concerns, which are key goals of event-driven design.

Option A is less appropriate because a single Lambda function that directly calls every downstream system becomes tightly coupled and harder to extend. Option C supports fan-out, but EventBridge provides richer routing, filtering, and event bus semantics for enterprise event-driven workflows. Option D is useful for workflow orchestration, but it creates a more centrally orchestrated pattern rather than the looser publish-and-subscribe model requested in the question.

AWS guidance for modern event-driven architectures emphasizes publishing events and allowing interested services to subscribe independently. EventBridge provides native routing and integration with many AWS services, making it easy to evolve the architecture over time. Therefore, B is the best answer for achieving loose coupling and future extensibility.

When sharing SSE-KMS–encrypted S3 objects across AWS accounts , AWS documentation states that three elements are required:

A customer-managed KMS key (Option E), because AWS-managed keys cannot be shared across accounts.

An S3 bucket policy (Option B) that grants the second account permission to access the objects.

A KMS key policy (Option C) that explicitly allows the second account to use the KMS key for decrypt operations.

Option A is incorrect because AWS-managed KMS keys cannot be shared. Option F uses SSE-S3, which does not use KMS and violates the encryption requirement. Option D is irrelevant because cross-account access does not require an S3 service role.

Therefore, the correct combination is B, C, and E .

The solution that will solve this problem is to create and inspect the Lambda dead-letter queue. Troubleshoot the failed functions. Reprocess the events. This way, the developer can identify and fix any issues that caused the Lambda function to fail when invoked asynchronously by API Gateway. The developer can also reprocess any orders that were not processed due to failures. The other options either do not address the root cause of the problem, or do not help recover from failures.

This workload is overwhelmingly read-heavy and repeatedly queries the same relatively static reference data (postal codes mapped to coordinates). The best way to reduce latency and offload Amazon RDS is to introduce an application-side cache using a lazy loading (cache-aside) pattern and choose a TTL that reflects how often the underlying data changes.

With lazy loading , the application first checks the cache for the postal code. If the value is present (a cache hit), the application returns the coordinates immediately with low latency and without querying the database. If the value is absent (a cache miss), the application reads from RDS, returns the result, and then populates the cache for subsequent requests. This pattern is operationally simple and widely used because the application controls what is cached and when.

A large TTL is appropriate here because postal-code-to-coordinate mappings typically do not change frequently. A longer TTL maximizes cache hit ratio, which is critical because each postal code is requested thousands of times per day. High cache hit rates reduce database read load, decrease query contention, and substantially improve response times. In contrast, a small TTL would cause frequent expirations and repeated database lookups, reducing the performance benefit and increasing database load.

Write-through and write-behind caching (options C and B) are designed to coordinate caching with writes . They are useful when write consistency and write performance are central concerns. Here, writes are rare; the performance issue is repeated reads. Write-behind can also introduce consistency delays, which is unnecessary for this scenario.

Therefore, the best change is D : implement a lazy loading (cache-aside) caching strategy with a large TTL to keep hot, rarely changing postal code lookups in cache and dramatically reduce read latency and database load.

Tracing Distributed Systems: AWS X-Ray is designed to trace requests across services, helping identify bottlenecks in distributed applications like this one.

No Code Changes: Enabling X-Ray tracing often requires minimal code changes, meeting the requirement.

Identifying Bottlenecks: Analyzing X-Ray traces and logs will reveal latency in communications between different AWS services, leading to the high duration time.

In DynamoDB, queries can only be performed on the Partition Key and Sort Key. If you need to perform high-performance queries on a non-key attribute, you must create a Global Secondary Index (GSI). A GSI allows you to define a new partition key (and optional sort key) for the existing data, enabling efficient queries on those attributes. Scans (Option C) are inefficient and get slower as data grows, regardless of parallelism.

Amazon API Gateway mock integrations allow developers to return static or templated responses without invoking any backend services . AWS documentation specifically recommends mock integrations for early API development and testing , when backend implementations are unavailable or incomplete.

With mock integrations, API Gateway itself generates responses based on mapping templates. This enables frontend and integration testing teams to validate request formats, response schemas, authentication, and error handling without deploying compute resources or writing application logic.

Options B, C, and D all require additional infrastructure or application logic, increasing engineering effort. Mock integrations require no backend code , no servers, and minimal configuration.

Therefore, using API Gateway mock integrations is the fastest and lowest-effort solution.

Requirement Summary:

Developer uses AWS IAM Identity Center (SSO) with AWS CLI / SDKs

Initially working fine

Now receiving AccessDenied errors

No changes to config or scripts

Key Understanding:

IAM Identity Center credentials are temporary and time-limited. When you use SSO-based access via the AWS CLI (aws sso login), it obtains temporary credentials stored in the local cache.

By default, these expire in 1 hour (can be extended).

Evaluate Options:

A. Permissions to CLI binary changed

Unlikely – this would produce execution errors, not AccessDenied from AWS API

B. Permission set lacks required permissions

Then the error would have occurred from the beginning, not after time

C. IAM Identity Center credentials expired

Most likely – user hasn ' t refreshed credentials using aws sso login again

After expiration, API calls fail with AccessDenied

D. Developer is calling the wrong AWS account

Would likely show different types of errors (AccountNotFound, etc.)

SSO and AWS CLI: https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-sso.html

Troubleshooting SSO CLI: https://docs.aws.amazon.com/cli/latest/userguide/sso-configure-profile-token.html

In CodePipeline, the service designed to run builds, execute unit/integration tests, and produce build artifacts (including test reports) is AWS CodeBuild. CodeBuild runs commands specified in a buildspec.yml file and supports reporting outputs such as test results (for example, JUnit XML), code coverage, and other build metadata.

Option A is correct because the developer can configure the CodeBuild project to run the test suite during the build phase and configure the buildspec to publish test report files. This integrates naturally into CodePipeline as a build stage, and the reports are generated consistently on each pipeline execution.

Option B is incorrect: CodeDeploy is for deploying applications (in-place/blue-green) and lifecycle hooks, not for standard build/test report generation.

Option C increases operational overhead by managing an EC2 build server, which is unnecessary when CodeBuild provides managed build environments.

Option D is unrelated: CodeArtifact is a package repository, not a test reporting solution.

Therefore, use CodeBuild and configure test reports via the buildspec.

A global secondary index is the correct way to support a new DynamoDB access pattern that uses a different partition key from the base table. The existing table is keyed by customerId and orderId, but the new Lambda function must query by order_date and orderId. An LSI cannot solve this because an LSI must use the same partition key as the base table and only allows an alternate sort key. A table scan is operationally inefficient and becomes expensive and slow as the table grows. DynamoDB Streams are for change data capture and event-driven processing, not for querying historical records by a new key pattern. A GSI with order_date as the partition key and orderId as the sort key directly supports efficient Query operations.

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Why Option A is Correct:Converting a Lambda function to use a container image involves packaging the function code into a container image, storing the image in Amazon Elastic Container Registry (ECR), and updating the function to use the ECR repository URI.

Why Other Options are Incorrect:

Option B: SAM templates support container-based Lambda deployment, but storing the image in S3 is not applicable.

Option C: CloudFormation does not natively support specifying Lambda container images in S3.

Option D: While partially correct, it omits the need to specify the image tag for the deployment.

AWS Documentation References:

Lambda Container Images

In DynamoDB, a ConditionalCheckFailedException occurs when an UpdateItem, PutItem, or DeleteItem operation includes a ConditionExpression that evaluates to false for the target item at the time of the write. This is expected behavior: DynamoDB uses conditional expressions for optimistic concurrency control and for enforcing business rules atomically at write time.

In this scenario, the developer wants to update only items whose status is Inactive by adding an email address. The most correct way to prevent conditional failures is to ensure the update request’s condition matches the intended target state. By adding (or correcting) a condition expression such as #st = :inactive (with expression attribute names/values), the update will proceed only when the item’s status is actually " Inactive " . Items that are " Active " will be skipped by design rather than causing unexpected failures in the workflow.

Option B checks whether the email attribute exists, but that does not enforce the key business rule (“only inactive items”) and may still fail depending on whether the attribute is present or absent across items. Option C (retry on ConditionalCheckFailedException) is not appropriate: the failure is not transient like throttling; it indicates the condition was not met, and retrying will likely fail again unless the item changes state. Option D is unrelated because email format validation is not what triggers DynamoDB conditional failures; the exception is about the conditional expression not matching item state.

Therefore, the correct fix is A : update the request’s conditional expression to explicitly check that status equals " Inactive " before applying the update. This ensures only the intended items are modified and eliminates unnecessary conditional failures.

CloudWatch custom metrics are the most cost-effective and operationally simple solution for tracking callback counts over time and alerting on thresholds. The application can publish a custom metric such as CallbackCount, and CloudWatch can retain metric data long enough for the required 10-day rolling view. CloudWatch alarms can notify administrators automatically when a threshold is breached. RDS would require database provisioning, schema management, queries, and separate alerting logic. X-Ray is for distributed tracing, not business metric aggregation and threshold alerting. Kinesis plus Lambda plus DynamoDB would work, but it is unnecessarily complex and more expensive for a simple count metric. AWS documentation confirms that custom metrics can be published to CloudWatch and alarms can send notifications when thresholds are breached. ( AWS Documentation )

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The goal is to reduce a given item’s price by 100 only when the resulting value would not fall below 500 . That means the update should occur only when the current Price is at least 600 (because Price - 100 > = 500 ⇒ Price > = 600). The requirement also says to do this with the least number of calls to DynamoDB. The most efficient approach is to use a single UpdateItem call with a ConditionExpression so DynamoDB enforces the rule atomically.

Option B does exactly that: issue one UpdateItem request that updates Price = Price - :delta (using an update expression) while including a condition like Price > = :minPriceBeforeReduction (where :minPriceBeforeReduction is 600). If the condition is true, DynamoDB performs the update; if it’s false, DynamoDB rejects the write with ConditionalCheckFailedException. This avoids a separate read/query call and prevents race conditions where the price could change between a read and a subsequent write.

Option A requires two calls (Query then UpdateItem) and is vulnerable to time-of-check/time-of-use issues unless additional conditional logic is added to the update anyway. Option C adds an extra condition on category, but the requirement is “reduce all inventory prices” subject to the minimum resulting price; filtering by category is unnecessary and can cause eligible items in other categories to be skipped. Option D expresses the rule in a mathematical form; while conceptually correct, DynamoDB condition expressions do not generally support arbitrary arithmetic in the condition the same way as a simple comparison, so the standard implementation is to compare against 600 directly.

Therefore, B is the correct choice: one UpdateItem call with a condition expression Price > = 600 minimizes DynamoDB calls and enforces the pricing rule safely and atomically.

To measure how long an application spends calling downstream AWS services (for example, DynamoDB, S3, SNS, etc.) in a distributed system, the most effective AWS-native approach is AWS X-Ray with appropriate instrumentation. X-Ray provides distributed tracing by capturing end-to-end request paths and breaking each request into segments and subsegments with precise timing information. When the application is instrumented with the X-Ray SDK, client handlers/interceptors automatically create subsegments for calls to AWS services, recording latency, response status, and error/throttle indicators.

This directly satisfies the requirement: determine the length of time of calls to various downstream services. In the X-Ray trace map and trace details, the developer can see per-service timing, identify which dependency is slow, and distinguish application processing time from downstream call time. This is specifically designed for performance bottleneck analysis and root cause investigation across distributed components.

Option A (VPC Flow Logs) captures network flow metadata (source/destination, ports, bytes, accept/reject), not application-level request timing to AWS service APIs, and it won’t show the latency breakdown per downstream service call.

Option B can work only if the application is already logging detailed timing for each call, but this is manual, inconsistent across services, and does not give a unified distributed trace view. It is also higher effort than using standard tracing instrumentation.

Option C increases CloudWatch metric granularity for EC2 but does not show per-request downstream service call durations.

Therefore, implementing AWS X-Ray with client handlers is the correct solution.

AWS Elastic Beanstalk supports several deployment policies, and in this case, the requirement is to forward a specific percentage of traffic to the new version without causing downtime. The Traffic-splitting deployment policy is the most appropriate choice.

Traffic-splitting Deployment: This deployment method allows you to gradually shift a specified percentage of incoming traffic from the old environment version to the new one. During the evaluation period, if any issues are detected, the traffic can be redirected back to the old version.

No Downtime: This method ensures no downtime since both versions of the application run concurrently, and traffic is split between them.

Alternatives:

Rolling deployments (Option A): These gradually replace instances but may result in partial downtime if some instances fail during deployment.

In-place deployments (Option C): In-place deployments replace instances without creating new ones, which can lead to downtime.

Immutable deployments (Option D): While this ensures no downtime by creating entirely new instances, it doesn ' t provide traffic splitting during the evaluation phase.

Elastic Beanstalk Deployment Policies

Lambda Concurrency: The ' maximum concurrency ' setting in event source mappings controls the maximum number of simultaneous invocations Lambda allows for that specific source.

Prioritizing Queues: Setting a lower maximum concurrency for the ' high priority queue ' ensures it ' s processed first while allowing more concurrent invocations from the ' low priority queue ' .

Batching: Batch size settings affect the number of messages Lambda retrieves from a queue per invocation, which is less relevant to the prioritization requirement.

The base table’s primary key is UserID, which means efficient Query operations on the table natively require specifying UserID (partition key equality). The required access pattern, however, is to retrieve many users for a given Location and then filter/sort by RegistrationDate (users who registered after a given date). Because Location is not part of the table’s primary key, the efficient DynamoDB approach is to create an index that supports this access pattern.

A global secondary index (GSI) can have a different partition key and sort key than the base table. By creating a GSI with Location as the partition key and RegistrationDate as the sort key, the application can use the Query operation to fetch all users in a specific location (Location = :loc) and apply a sort key condition such as RegistrationDate > :date (or BETWEEN), which is efficient and avoids a full-table scan. This design is the canonical pattern for optimizing DynamoDB queries: model the table and indexes around known access patterns so the application can use Query instead of Scan.

Option C (LSI) is not valid for this requirement because an LSI must use the same partition key as the base table (UserID). Since the access pattern is by Location, an LSI cannot make Location the partition key. Option B (Scan + filter) is inefficient at scale because Scan reads every item (or every item in the scanned segments) and then filters results, consuming read capacity and increasing latency. Option D (BatchGetItem) requires the caller to already know the primary keys of the items to retrieve; it cannot discover “all users in a location after a date” and does not support filtering in the way Query on an index does.

Therefore, A is the correct and most efficient solution: a GSI on (Location, RegistrationDate) and Query with a date range condition.

The command that will meet the requirements is sam sync -watch. This command enables AWS SAM Accelerate mode, which allows the developer to only redeploy the Lambda functions that have changed. The -watch flag enables file watching, which automatically detects changes in the source code and triggers a redeployment. The other commands either do not enable AWS SAM Accelerate mode, or do not redeploy the Lambda functions automatically.

The bottleneck is the database read path, shown by the sudden increase in the RDS ReadLatency metric. Because the application performs more reads than writes, caching frequently requested query results with Amazon ElastiCache is the most direct way to reduce database load and improve response time. Scaling ECS tasks or instances might increase application capacity, but it can also increase read pressure on the database and worsen latency. RDS for MySQL does not use DynamoDB-style read capacity units, so option C is invalid. Increasing ECS task memory helps only if the application containers are memory constrained, which is not indicated. AWS performance guidance identifies caching as a way to improve read latency, read throughput, and efficiency for read-heavy workloads. ( AWS Documentation )

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Mocking API Responses: API Gateway ' s Mock integration type enables simulating API behavior without invoking backend services.

Testing with Sample Data: Using captured responses from the real third-party API ensures realistic testing of the integration code.

Focus on Integration Logic: This solution allows the developer to isolate and test the application ' s interaction with the payment APIs, even without a test environment from the third-party providers.

The simplest and most efficient way to avoid hardcoding configuration such as a DynamoDB table name is to use Lambda environment variables. Environment variables are designed for runtime configuration and allow changing values without updating application code logic. The developer can store the table name in an environment variable (for example, TABLE_NAME) and read it from the runtime environment using the standard language method (such as os.environ in Python, process.env in Node.js, etc.).

This approach has very low operational overhead: updating the table name becomes a configuration change (in the Lambda console, IaC templates, or CI/CD pipeline) rather than a code change. It also keeps deployment packages stable and avoids unnecessary dependencies.

Option B is not suitable because /tmp is ephemeral storage that can be cleared between invocations and is not intended for configuration management.

Option C is heavier than necessary. Lambda layers are great for shared libraries and dependencies, but using a layer to store a single changing table name is awkward and forces a layer update and function reconfiguration when the value changes.

Option D does not solve the problem because a global variable is still set in the code. If the table name changes, the code must still be modified and redeployed.

Therefore, storing the table name in a Lambda environment variable is the most efficient solution.

S3 Object Lambda allows you to add your own code to process data retrieved from S3 before returning it to an application. You can use an AWS Lambda function to modify the data, such as removing PII, redacting confidential information, or resizing images. You can create an S3 Object Lambda access point and associate it with your Lambda function. Then, you can use the access point to request objects from S3 and get the modified data back. This way, you can maintain only one copy of the original document in S3 and apply different transformations depending on who accesses it. Reference: Using AWS Lambda with Amazon S3

This solution meets the requirements most cost-effectively because it optimizes the photos on demand and caches them for future requests. Lambda@Edge allows the developer to run Lambda functions at AWS locations closer to viewers, which can reduce latency and improve photo quality. The developer can create a Lambda@Edge function that uses the GET parameters from each request to optimize the photos with the required dimensions and resolutions and returns them as a response. The function can also store a copy of the processed photos on Amazon S3 for subsequent requests, which can reduce processing time and costs. Using S3 Batch Operations to create new variants of the photos will incur additional storage costs and may not cover all possible dimensions and resolutions. Creating a dynamic CloudFront origin or a Lambda@Edge function to route requests to corresponding photo variants will require maintaining a mapping of device types and photo variants, which can be complex and error-prone.

This is a cross-account access pattern using STS AssumeRole. Account A owns the DynamoDB table and has created an IAM role (AccessPII) with permissions to access the table. Account A also configured a trust policy that allows principals from Account B to assume the role. That trust policy alone is not enough; the caller in Account B must also be allowed to call sts:AssumeRole.

Therefore, in Account B, the EC2 instance profile role (the role attached to the EC2 instances running the app) must have permission to assume the role in Account A. That is Option A.

Next, the application must actually obtain temporary credentials for the Account A role. The standard way is to call STS AssumeRole in the application logic (or use an SDK credential provider that assumes a role). This yields temporary credentials that have the permissions of the AccessPII role, allowing DynamoDB access in Account A. That is Option D.

Option B is not correct because granting direct DynamoDB table permissions in Account B does not grant access to a table owned by Account A unless Account A also grants access via a resource policy (DynamoDB resource policies exist but the scenario already sets up role assumption; the intended solution is AssumeRole).

Option C is incomplete/wrong: getting temporary credentials from the EC2 role alone gives permissions of the EC2 role in Account B, not the permissions in Account A. You must assume the cross-account role.

Option E is for IAM user MFA session tokens, not cross-account role assumption for an EC2 role.

Therefore, grant sts:AssumeRole to the EC2 role and call AssumeRole in code.

Amazon API Gateway is a service that enables developers to create, publish, maintain, monitor, and secure APIs at any scale. The developer can define a development stage on the API Gateway API and instruct the other developers to point the endpoints to the development stage. This way, the developer can provide beta access to the new version of the API without affecting customers who use the production stage. This solution will meet the requirements with the least operational overhead.

CloudWatch Logs data protection policies are designed to detect and mask sensitive data in log events. Managed data identifiers can detect common categories of sensitive information, including PII-related data types, and masking applies across log viewing and query workflows. The logs:Unmask permission controls who can view original unmasked data, so only approved security engineers should receive that permission. Updating the Lambda function is good hygiene but does not protect historical logs and depends on application correctness. KMS encryption protects logs at rest, but authorized log readers would still see plaintext after decryption. Firehose and Athena add unnecessary pipeline complexity and do not enforce masking inside CloudWatch Logs Insights or subscription filters. AWS documentation confirms data protection policies mask sensitive log data and logs:Unmask is required to view unmasked data. ( AWS Documentation )

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The correct answer is C because Amazon SNS FIFO topics are designed for applications that require both message ordering and deduplication , and high throughput mode extends the throughput capability to support large-scale workloads such as sending thousands of notifications per second . This directly matches the requirement for ordered notification delivery at very high scale.

The requirement to filter out some notifications before sending them to the HTTP endpoint is also handled natively by SNS subscription filter policies . With a filter policy, only messages with matching message attributes are delivered to the subscribed endpoint. This means the application can publish all notification events to the SNS topic, while SNS ensures that only relevant notifications are forwarded to the HTTP subscriber. This avoids unnecessary downstream traffic and reduces custom logic.

Option A is incorrect because Amazon Data Firehose is intended primarily for streaming data delivery to analytics and storage destinations, not for ordered notification delivery to HTTP endpoints with filtering requirements. Option B is incorrect because Amazon SQS standard queues do not preserve strict ordering . Also, this design adds Lambda processing complexity and does not align as cleanly with native FIFO and filtering capabilities. Option D is not the best choice because EventBridge supports filtering and API destinations, but it does not provide the same straightforward FIFO ordered delivery guarantee required here.

Therefore, Amazon SNS FIFO in high throughput mode with an HTTP subscription and subscription filter policy is the AWS service combination that best satisfies all the stated requirements.

To securely grant temporary access to a specific S3 object, the best option is to use an Amazon S3 presigned URL . A presigned URL is created by an IAM principal (user or role) that already has permission to access the object. The URL includes a signature and an expiration time , allowing anyone who has the URL to perform the specified operation (typically GET to download, or PUT to upload) until the URL expires . This meets the requirement for temporary access without changing the bucket to be publicly accessible.

Presigned URLs are especially useful when the developer needs to share access with people who do not have AWS credentials or are not part of the same AWS account. The developer can set an expiration that matches the business need (minutes to hours, etc.). After expiration, the URL no longer works, which reduces security exposure compared with long-lived access keys or permanently permissive bucket policies.

Option C (bucket policy with time restriction) can enforce time-based access, but it is not a practical way to securely share with a “specific group of people” unless those people authenticate with identifiable principals (IAM roles/users, federated identities) and you scope the policy to those principals. Simply providing the bucket S3 URL would not grant access unless the recipients also have valid permissions. Option D (static website hosting) is generally for public web content and does not inherently provide secure, temporary, per-object access control. Option A is unrelated to access sharing; object retention and restore operations are for retention/compliance and archival retrieval, not temporary permission grants.

Therefore, generating and sharing a presigned URL (B) is the standard, secure mechanism for time-limited access to S3 objects.

API Gateway mock integration is designed for exactly this situation: generating API responses directly from API Gateway without requiring a working backend. This lets dependent teams test immediately while the real backend is still being developed. The developer can configure the integration request and integration response mapping to return specific payloads and status codes. Request validators check incoming requests but do not generate backend responses. Gateway responses customize API Gateway-generated error responses, not normal business responses for application testing. A Lambda authorizer controls access and would require a Lambda function, which adds unnecessary implementation work. AWS documentation confirms that mock integrations let API developers generate responses directly from API Gateway and unblock teams before backend development is complete. ( AWS Documentation )

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The correct answers are B and E .

The existing platform already runs on Kubernetes , so the fastest and most compatible migration path is to use Amazon EKS , which is AWS’s managed Kubernetes service. This preserves the application’s orchestration model and minimizes refactoring. Because the clusters must be highly available , EKS is a strong fit since it is designed to run Kubernetes workloads across multiple Availability Zones.

For shared storage, the on-premises application currently depends on a common NFS share that all containers can access. On AWS, the service that most closely matches this requirement is Amazon EFS , which provides a managed, elastic, shared file system that supports NFS access and can be mounted concurrently from multiple compute nodes. By contrast, Amazon EBS is block storage that is generally attached to a single instance and is not the right replacement for a shared multi-node NFS workload.

Therefore, B is correct because it moves the shared NFS data to Amazon EFS and stores the container images in Amazon ECR , which is the proper AWS managed registry for container images. E is correct because it runs the applications on Amazon EKS and uses Amazon EFS as the shared mounted file system across the Kubernetes worker nodes.

Option C is wrong because it uses ECS , not Kubernetes. Option D is wrong because EBS does not meet the requirement for a shared NFS-like file system across highly available Kubernetes nodes. Option A is wrong for the same storage reason.

So, the best migration combination is Amazon EFS + Amazon EKS , which makes B and E correct.

This solution will solve the deployment issue by deploying the new version of the application to one new EC2 instance at a time, while keeping the old version running on the existing instances. This way, there will always be at least four instances serving traffic during the deployment, and no downtime or performance degradation will occur. Option A is not optimal because it will increase the cost of running the Elastic Beanstalk environment without solving the deployment issue. Option B is not optimal because it will split the traffic between two versions of the application, which may cause inconsistency and confusion for the customers. Option D is not optimal because it will deploy the new version of the application to two existing instances at a time, which may reduce the capacity below four instances during the deployment.

The correct answer is C because reserved concurrency guarantees that a specific AWS Lambda function has access to a defined number of concurrent executions. In this scenario, the function normally runs correctly but occasionally receives a surge of up to 500 S3 object-created events per minute . Since each invocation runs for about 30 seconds , the function may need substantial concurrency during those bursts. Configuring reserved concurrency ensures that this function has dedicated execution capacity available during peak periods and is not affected by concurrency consumption from other functions in the account.

AWS documentation explains that reserved concurrency both sets the maximum concurrency for a function and reserves that capacity exclusively for the function. This is especially helpful when one function must continue operating reliably under burst traffic. Even though other functions are currently running as expected, reserved concurrency is the direct AWS mechanism to protect the processing function from account-level concurrency contention during spikes.

Option A is incorrect because the function already completes in about 30 seconds; changing timeout does not solve high-volume invocation scaling. Option B is not the best answer because adding a Lambda layer does not guarantee improved throughput or concurrency handling. Option D is incorrect because decreasing memory usually reduces available CPU and can make performance worse, which would increase the chance of delays or failures.

Therefore, the most effective approach is to configure reserved concurrency for the processing function so it can continue to run reliably during high-volume object upload periods.

Amazon DynamoDB throttling during peak periods is commonly caused by hot partitions . In this scenario, using order_date as the partition key results in a large number of writes targeting the same partition during peak ordering times. Even in on-demand mode, DynamoDB enforces per-partition limits, which leads to throttling when traffic is unevenly distributed.

AWS best practices emphasize designing partition keys that provide high cardinality and uniform access patterns . Using customerId as the partition key distributes write traffic across many partitions because customer IDs naturally vary and scale with the number of users.

Switching to provisioned capacity (Option B) does not solve the root cause and can still result in throttling due to uneven access patterns. Sequential partition keys (Option A) worsen the hot partition problem. Migrating to Aurora (Option C) is unnecessary and does not address DynamoDB design issues.

AWS documentation clearly states that access pattern design is critical , and changing the partition key to better distribute workload is the correct solution. Therefore, using customerId as the partition key resolves throttling and aligns with DynamoDB best practices.

The AWS Serverless Application Model Command Line Interface (AWS SAM CLI) is a command-line tool for local development and testing of Serverless applications3. The sam local generate-event command of AWS SAM CLI generates sample events for automated tests3. The sam local invoke command is used to invoke Lambda functions3. Therefore, option C is correct.

The workload is explicitly very I/O intensive and requires reading the same file multiple times. Reading repeatedly from S3 across the network will introduce latency and repeated network I/O. For best performance, the function should download the file once to fast local storage and perform all repeated reads locally.

AWS Lambda provides ephemeral storage mounted at /tmp. AWS allows increasing this ephemeral storage beyond the default size. Because the files are at most 2 GB, the developer can configure the Lambda function’s ephemeral storage to 2 GB and copy each S3 object into /tmp before processing. This converts repeated network reads into repeated local filesystem reads, which is significantly faster and more consistent for I/O-heavy workloads.

Option D (read directly from S3) is the least optimized because each pass over the file requires additional network I/O and S3 GET requests.

Option A is not feasible: Lambda cannot directly attach and manage an EBS volume like an EC2 instance can.

Option B is not applicable: ENA is an EC2 networking feature; Lambda networking is managed by the service and does not allow attaching ENAs for performance tuning in that way.

Because the files are disposable after processing, using ephemeral /tmp storage is also cost-effective and operationally simple, and it is aligned with the “process once per file” pattern.

Therefore, increase Lambda ephemeral storage and copy the video into /tmp for repeated reads.

Comprehensive and Detailed Explanation (250–300 words):

For in-place deployments , AWS CodeDeploy updates instances directly rather than using blue/green infrastructure. AWS documentation states that when automatic rollback is enabled and a deployment fails (for example, due to failed health checks or validation scripts), CodeDeploy initiates a rollback deployment .

In an in-place rollback, CodeDeploy redeploys the last known successful application revision to the affected instances. This rollback is treated as a new deployment , complete with its own deployment ID and lifecycle events. CodeDeploy does not use snapshots, Route 53 switching, or external promotion logic for in-place deployments.

Options A and B describe mechanisms that are used in other services or blue/green strategies, not in-place deployments. Option D is incorrect because CodePipeline orchestrates stages but does not perform rollback logic for CodeDeploy.

Therefore, redeploying the previous stable version as a new deployment is the correct behavior.

Elastic Beanstalk application version lifecycle settings can delete old application versions from Elastic Beanstalk records and can optionally delete the source bundle from the S3 bucket. To retain the source code, the lifecycle policy must be configured not to delete the source bundle from S3. Enabling S3 versioning is not the correct control because Elastic Beanstalk itself can be configured to retain or delete source bundles. Increasing the version quota only changes how many application versions Elastic Beanstalk tracks before lifecycle cleanup occurs; it does not guarantee S3 source bundle retention. Disabling a separate S3 lifecycle policy is not the targeted Elastic Beanstalk setting. AWS documentation states that when Elastic Beanstalk deletes an application version, the source bundle remains in S3 unless the rule is configured to delete it. ( AWS Documentation )

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The correct answer is B because Amazon S3 Lifecycle rules can manage versioned objects, including noncurrent object versions . In a versioning-enabled bucket, the newest object version is the current version, and older ones become noncurrent versions. The requirement is to keep the current version and only the immediately previous version. S3 Lifecycle is the native feature designed to expire or retain noncurrent versions according to policy.

AWS documentation explains that lifecycle configuration for versioned buckets can specify how many newer noncurrent versions to retain before Amazon S3 permanently removes older noncurrent versions. This directly matches the requirement to preserve only the latest prior version while cleaning up older historical versions automatically.

Option A is incorrect because an S3 bucket policy controls access permissions, not retention behavior for object versions. Option C is incorrect because S3 Object Lock is intended for write-once-read-many retention and legal hold scenarios, not for maintaining only one previous object version. Option D is not appropriate because suspending versioning would stop creation of new full versions going forward, but it would not provide an automated retention policy for existing versions, and modifying the application adds unnecessary complexity.

Using an S3 Lifecycle rule is the most operationally efficient and AWS-native solution. It allows the bucket to continue using versioning for protection against accidental overwrites while automatically limiting retained noncurrent versions to only what the business needs. Therefore, B is the correct answer.

To ensure a Lambda function URL is reachable only through CloudFront, the solution must (1) make CloudFront the only authorized caller, and (2) prevent direct public access to the function URL endpoint. The AWS-native way to do this is to use a resource-based policy on the Lambda function that permits invocation only from the specific CloudFront distribution, combined with CloudFront’s Origin Access Control (OAC) to sign origin requests.

A Lambda function URL can be configured for IAM-based authorization, and Lambda supports resource-based permissions (via lambda:AddPermission) that restrict who can invoke it. By scoping the permission so that only the CloudFront distribution (identified by a source ARN / distribution identifier) is allowed, direct requests that do not come through CloudFront will be rejected. This enforces the requirement that the function URL cannot be accessed directly.

CloudFront OAC is the modern mechanism to securely access origins by having CloudFront sign the requests it sends to the origin. When CloudFront signs requests and the origin (here, the Lambda function URL) is configured to accept only authorized requests per its resource policy, CloudFront becomes the only viable access path.

Option A is incorrect because CloudFront does not use a “resource-based policy” to grant access to an origin in this way; the enforcement must happen at the origin. Option C is not how CloudFront accesses origins; CloudFront does not assume a customer IAM role to fetch origin content. Option D is fragile and not recommended: CloudFront IP ranges can change and are not intended to be used as a static allowlist for origin protection; additionally, IP-based controls do not provide the same strong identity-based authorization that OAC + resource policy provides.

Therefore, B is the correct solution: restrict the Lambda function URL with a Lambda resource-based policy and configure CloudFront with OAC so only CloudFront-originated requests are accepted.

Comprehensive and Detailed Explanation (AWS Verified – 250–300 Words)

When an Amazon S3 bucket is configured with default encryption using SSE-KMS, AWS Key Management Service (KMS) plays an active role in every PutObject request. Although the IAM user already has the s3:PutObject permission, this alone is not sufficient when SSE-KMS is enabled.

According to AWS documentation, when a client uploads an object to an S3 bucket encrypted with SSE-KMS, Amazon S3 must call AWS KMS on behalf of the caller to generate a unique data encryption key. This operation requires the kms:GenerateDataKey permission on the KMS key used for encryption. If this permission is missing, the PutObject request fails with an Access Denied error—even though S3 permissions appear correct.

AWS explicitly states that IAM principals uploading objects to SSE-KMS–encrypted buckets must be allowed to use the KMS key. At a minimum, the following KMS permissions are required:

kms:GenerateDataKey

(Implicitly) access allowed by the KMS key policy

Option C correctly resolves the issue by granting the IAM user permission to generate a data key via AWS KMS, enabling successful encryption during the upload process.

Option A is invalid because s3:EncryptionConfiguration is not required for object uploads.

Option B is unnecessary because the IAM user already has s3:PutObject.

Option D is incorrect because ACLs are not used to control KMS encryption permissions and are discouraged by AWS best practices.

API Gateway Stages allow you to maintain multiple versions of your API (e.g., dev, test, prod) with unique URLs. Using a Lambda Alias (like " DEV " ) allows the API Gateway stage to point to a specific version of the Lambda code without affecting production traffic. Mock Integrations in API Gateway allow you to simulate responses for external APIs (like the payment processor) during testing. This combination provides environment isolation and a mechanism (aliases) for safe promotion and rollback.

A Lambda layer is the correct operationally efficient solution because layers are specifically designed to package reusable code, libraries, custom runtimes, or configuration files separately from function code. The developer can publish the shared .zip archive as a layer version and attach that layer to multiple Lambda functions. This avoids copying the same dependency into every deployment package and centralizes dependency management. Amazon S3 alone does not create an importable runtime dependency path for Lambda. A separate Lambda function would add synchronous invocation latency and coupling. A container image could work for container-based functions, but it is heavier and unnecessary when the code is already packaged as a .zip dependency. AWS documentation states that layers are used to separate reusable dependencies from function code and add them to functions. ( AWS Documentation )

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For API Gateway REST APIs, configuration changes (new resources/methods) do not automatically become callable from a stage until a new API Gateway Deployment is created and associated with the stage. In CloudFormation, an AWS::ApiGateway::Deployment resource is immutable and effectively represents a snapshot of the API configuration at a point in time. If the Deployment resource does not change (for example, its logical ID or a property that forces replacement), CloudFormation may not create a new deployment even though the stack update succeeds—resulting in the stage still pointing to the old deployment. The symptom is exactly what’s described: new methods return 404 because they are not part of the deployed snapshot.

A common operational fix is to create a new deployment explicitly during CI/CD. Option C does this by running aws apigateway create-deployment, which creates a fresh deployment that includes the new resources/methods and makes them available on the stage.

Option D is unrelated (CloudFront invalidation).

Options A and B do not affect API Gateway deployment snapshots.

💡 In pure CloudFormation, another common pattern is to force a new deployment by changing the Deployment logical ID or embedding a timestamp/hash in the deployment description. But among the options given, running create-deployment is the direct fix.

Therefore, add a pipeline step to run aws apigateway create-deployment.

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:

In this scenario, the developer is configuring a mock integration in API Gateway. The integration request mapping template allows you to map the incoming request data to a format that the API expects. For mock integration, it ' s common to return specific HTTP status codes based on the conditions.

Using $context.integration.status: The $context.integration.status variable refers to the status of the API Gateway integration, which is useful for generating responses based on the condition. Option D correctly uses this variable to determine the HTTP status code, returning 200 for a successful mock request or 500 for a failure.

Alternatives:

Options A, B, and C do not use the correct context variables for handling mock integrations. These options would not return the correct status codes based on the actual integration status.

API Gateway Mapping Templates and Accessing Context Variables

This solution will meet the requirements by using DynamoDB Accelerator (DAX), which is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10 times performance improvement - from milliseconds to microseconds - even at millions of requests per second. The developer can use DAX to cache the trading data that is used to process each trading request, which will reduce the data retrieval time with the least possible effort. Option A is not optimal because it will add local secondary indexes (LSIs) for the trading data, which may not improve the performance or reduce the latency of data retrieval, as LSIs are stored on the same partition as the base table and share the same provisioned throughput. Option B is not optimal because it will store the trading data in Amazon S3 and use S3 Transfer Acceleration, which is a feature that enables fast, easy, and secure transfers of files over long distances between S3 buckets and clients, not between DynamoDB and clients. Option C is not optimal because it will add retries with exponential backoff for DynamoDB queries, which is a strategy to handle transient errors by retrying failed requests with increasing delays, not by reducing data retrieval time.

The requirement is to redact PII before the object content reaches the consumer, while the analytics service continues to use standard S3 GET requests. The most operationally efficient way is Amazon S3 Object Lambda.

S3 Object Lambda lets you add your own code to process and transform data as it is retrieved from S3. Instead of the analytics service reading directly from the bucket, it reads through an S3 Object Lambda Access Point. When the service performs a GET request, S3 invokes the associated Lambda function, which can modify the object payload on the fly—for example, by calling an external or internal PII redaction API and returning a redacted version of the report. The original object remains unchanged in the bucket, while consumers receive the transformed (redacted) response.

This approach is operationally efficient because it avoids:

duplicating and storing redacted copies of every report,

refactoring the analytics service to use a different datastore or ingestion path,

building a separate proxy service layer.

Option A requires significant refactoring and ongoing ETL/warehouse ops.

Option C provides confidentiality via encryption but does not perform redaction (analytics would still see PII after decryption).

Option D changes the access pattern completely (analytics no longer uses GET) and adds messaging complexity without directly returning redacted object content.

Therefore, S3 Object Lambda + Object Lambda Access Point is the most efficient solution to redact data at retrieval time.

To determine who deleted an Amazon RDS DB instance, the correct source of truth is AWS CloudTrail, which records API activity and includes the identity (IAM user, role, assumed role session) that made the call. Deleting an RDS instance is performed through the RDS API action DeleteDBInstance, and CloudTrail logs an event for this action that contains key fields such as userIdentity, eventTime, eventName, sourceIPAddress, and request parameters identifying the DB instance (for example, dBInstanceIdentifier).

Because the DB instance was deleted within the past 90 days, CloudTrail Event History is commonly sufficient (Event History typically retains 90 days of management events). If the company has a CloudTrail trail configured to deliver logs to S3/CloudWatch Logs, those logs can also be queried for the same event.

Option A directly matches this: retrieve CloudTrail events for DeleteDBInstance related to mysql-db and inspect the userIdentity section to identify the IAM principal that performed the deletion.

Option B is not reliable because RDS log groups (when enabled) capture database engine logs (slow query log, error log, general log) and do not record control-plane actions like who deleted the instance.

Option C is incorrect because X-Ray traces application-level request flows; it does not audit administrative actions like RDS deletion.

Option D is not applicable: Systems Manager inventory does not provide authoritative records of RDS deletions or the IAM principal responsible.

Therefore, CloudTrail lookup for DeleteDBInstance events is the correct solution.

This solution will meet the requirements by storing the Root CA Cert as a Secure String parameter in AWS Systems Manager Parameter Store, which is a secure and scalable service for storing and managing configuration data and secrets. The resource-based policy will allow IAM users in different AWS accounts and environments to access the parameter without requiring cross-account roles or permissions. The Lambda code will be refactored to load the Root CA Cert from the parameter store and modify the runtime trust store outside the Lambda function handler, which will improve performance and reduce latency by avoiding repeated calls to Parameter Store and trust store modifications for each invocation of the Lambda function. Option A is not optimal because it will use AWS Secrets Manager instead of AWS Systems Manager Parameter Store, which will incur additional costs and complexity for storing and managing a non-secret configuration data such as Root CA Cert. Option C is not optimal because it will deactivate the application secrets and monitor the application error logs temporarily, which will cause application downtime and potential data loss. Option D is not optimal because it will modify the runtime trust store inside the Lambda function handler, which will degrade performance and increase latency by repeating unnecessary operations for each invocation of the Lambda function.

Why Option C is Correct:Implementing a connection pool in the Redis client reduces connection overhead and avoids frequent connection establishment, which helps to reduce latency and connection failures.

Why Other Options are Incorrect:

Option A: Exponential backoff retry strategies help with transient failures but do not resolve latency caused by frequent connection establishment.

Option B: Storing scores in application memory adds complexity and risks inconsistency.

Option D: Adding more nodes is unnecessary unless the cluster is under heavy load. Latency due to connection failures is better addressed at the application level.

AWS Documentation References:

Amazon ElastiCache Best Practices

Lambda does not allow the developer to select an EC2 instance type, so option A is invalid. For Lambda, CPU allocation scales proportionally with memory. If the image-processing workload is CPU-bound or memory-constrained, increasing the configured memory can also increase available CPU power and reduce execution duration. Updating the Python runtime may provide minor improvements but is not the primary Lambda performance control. Reserved concurrency controls how many concurrent executions are reserved for the function; it does not make one invocation process faster. The issue is that each run takes too long, not that the function lacks concurrency. AWS Lambda documentation confirms that increasing function memory also increases CPU allocation, and memory can be tuned to improve performance. ( AWS Documentation )

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Containerization: Packaging the application as a container image promotes portability and standardization. Docker is the standard tool for containerization.

Amazon ECR: ECR is a managed container registry designed to work seamlessly with AWS container services.

Fargate: ECS Fargate provides serverless container orchestration, minimizing operational overhead for this proof-of-concept.

Centralized Logging Benefits: Centralized logging is essential for operational visibility in scalable systems, especially those using multiple EC2 instances like our e-commerce website. CloudWatch provides this capability, along with other monitoring features.

CloudWatch Agent: This is the best way to send custom application logs from EC2 instances to CloudWatch. Here ' s the process:

Install the CloudWatch agent on each EC2 instance.

Configure the agent with a configuration file, specifying:

Which log files to collect.

The format in which to send logs to CloudWatch (e.g., JSON).

The specific CloudWatch Logs log group and log stream for these logs.

Viewing and Analyzing Logs: Once the agent is pushing logs, use the CloudWatch Logs console or API:

View and search the logs across all instances.

Set up alarms based on log events.

Use CloudWatch Logs Insights for sophisticated queries and analysis.

Amazon EC2 instances can send the state-change notification events to Amazon EventBridge. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/monitoring-instance-state-changes.html Amazon EventBridge can send and receive events between event buses in AWS accounts. https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-cross-account.html

Secrets Management: AWS Secrets Manager is designed specifically for storing and managing sensitive credentials.

Built-in Rotation: Secrets Manager provides automatic secret rotation functionality, enhancing security posture significantly.

IAM Integration: IAM policies and roles grant fine-grained access to ECS Fargate, ensuring the principle of least privilege.

Reduced Overhead: This solution centralizes secrets management and automates rotation, reducing operational overhead compared to the other options.

This solution will meet the requirements by using the Time to Live (TTL) feature of DynamoDB, which enables automatically deleting items from a table after a certain time period. The developer can add a new attribute of type Number that has a timestamp that is set to 48 hours after the blog post creation time, which represents the expiration time of the item. The developer can configure the DynamoDB table with a TTL that references the new attribute, which instructs DynamoDB to delete the item when the current time is greater than or equal to the expiration time. This solution is also cost-effective as it does not incur any additional charges for deleting expired items. Option A is not optimal because it will create a script to find and remove old posts with a table scan and a batch write item API operation, which may consume more read and write capacity units and incur more costs. Option B is not optimal because it will use Amazon Elastic Container Service (Amazon ECS) and AWS Fargate to run the script, which may introduce additional costs and complexity for managing and scaling containers. Option C is not optimal because it will create a global secondary index (GSI) that uses the expiration time as a sort key, which may consume more storage space and incur more costs.

Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer References:

1. Understanding the Use Case:

The developer needs to export DynamoDB table data into Amazon S3 buckets using the AWS CLI, and some exports are failing. Proper credentials and permissions have already been configured.

2. Key Conditions to Check:

Region Consistency:DynamoDB exports require that the target S3 bucket and the DynamoDB table reside in the same AWS Region. If they are not in the same Region, the export process will fail.

Point-in-Time Recovery (PITR):PITR is not required for exporting data from DynamoDB to S3. Enabling PITR allows recovery of table states at specific points in time but does not directly influence export functionality.

DynamoDB Streams:Streams allow real-time capture of data modifications but are unrelated to the bulk export feature.

DAX (DynamoDB Accelerator):DAX is a caching service that speeds up read operations for DynamoDB but does not affect the export functionality.

3. Explanation of the Options:

Option A: " Ensure that point-in-time recovery is enabled on the DynamoDB tables. " While PITR is useful for disaster recovery and restoring table states, it is not required for exporting data to S3. This option does not address the export failure.

Option B: " Ensure that the target S3 bucket is in the same AWS Region as the DynamoDB table. " This is the correct answer. DynamoDB export functionality requires the target S3 bucket to reside in the same AWS Region as the DynamoDB table. If the S3 bucket is in a different Region, the export will fail.

Option C: " Ensure that DynamoDB streaming is enabled for the tables. " Streams are useful for capturing real-time changes in DynamoDB tables but are unrelated to the export functionality. This option does not resolve the issue.

Option D: " Ensure that DynamoDB Accelerator (DAX) is enabled. " DAX accelerates read operations but does not influence the export functionality. This option is irrelevant to the issue.

4. Resolution Steps:

To ensure successful exports:

Verify the Region of the DynamoDB tables:

Check the Region where each table is located.

Verify the Region of the target S3 buckets:

Confirm that the target S3 bucket for each export is in the same Region as the corresponding DynamoDB table.

If necessary, create new S3 buckets in the appropriate Regions.

Run the export command again with the correct setup:

aws dynamodb export-table-to-point-in-time \

--table-name < TableName > \

--s3-bucket < BucketName > \

--s3-prefix < Prefix > \

--export-time < ExportTime > \

--region < Region >

Amazon SNS supports server-side encryption (SSE) using AWS KMS to protect message content at rest . Enabling SSE on the SNS topic ensures that all messages published to the topic are encrypted using a customer-managed or AWS-managed KMS key. AWS documentation explicitly states that SSE is the mechanism for encrypting SNS messages stored within the service.

For encryption in transit , AWS services rely on HTTPS/TLS. To enforce this at the IAM policy level, AWS recommends using the global condition key aws:SecureTransport . By adding an explicit Deny statement when " aws:SecureTransport " : " false " , the policy ensures that any request not using HTTPS is rejected. This is a common AWS security best practice and is documented across multiple AWS services.

Option E correctly enforces encryption in transit. Option B is incorrect because denying when SecureTransport is true would block valid HTTPS requests. Option D is invalid because sns:Protocol applies to subscription protocols (such as email or HTTP endpoints), not to Lambda publishing actions. Option C (VPC endpoint) is not required because Lambda-to-SNS traffic is already encrypted over TLS by default.

Therefore, enabling SNS server-side encryption and enforcing HTTPS via an IAM deny condition fully satisfies the encryption-at-rest and encryption-in-transit requirements.

DynamoDB Accelerator (DAX) provides a managed caching layer specifically optimized for DynamoDB, reducing latency for repeated read requests.

Why Option A is Correct:

Purpose-Built: DAX is designed for DynamoDB, enabling sub-millisecond response times for frequently accessed items.

Operational Efficiency: No need for additional application-level caching logic.

Why Not Other Options:

Option B: Auto Scaling increases capacity but does not address repetitive reads.

Option C: API Gateway caching helps reduce request processing time but does not optimize DynamoDB reads.

Option D: ElastiCache is a general-purpose cache, adding unnecessary complexity for DynamoDB use cases.

DynamoDB Accelerator (DAX)

The GenerateDataKey API returns a data key that is encrypted under a symmetric encryption KMS key that you specify, and a plaintext copy of the same data key1. The data key is a random byte string that can be used with any standard encryption algorithm, such as AES or SM42. The plaintext data key can be used to encrypt or decrypt data outside of AWS KMS, while the encrypted data key can be stored with the encrypted data and later decrypted by AWS KMS1.

In this scenario, the developer needs to use the GenerateDataKey API to encrypt the PDF file so that it can be decrypted later. The developer also needs to use an AWS KMS symmetric customer managed key for encryption. To achieve this, the developer can follow these steps:

Call the GenerateDataKey API with the symmetric customer managed key ID and the desired length or specification of the data key. The API will return an encrypted data key and a plaintext data key.

Write the encrypted data key to disk for later use. This will allow the developer to decrypt the data key and the PDF file later by using AWS KMS.

Use the plaintext data key and a symmetric encryption algorithm to encrypt the PDF file. The developer can use any standard encryption library or tool to perform this operation, such as OpenSSL or AWS Encryption SDK.

Discard the plaintext data key from memory as soon as possible after using it. This will prevent unauthorized access or leakage of the data key.

Because the Lambda function is successfully triggered by the S3 event notification, the invocation path (S3 → Lambda) is working correctly. The failure occurs specifically when the function tries to write to DynamoDB, which strongly indicates an authorization problem rather than an invocation, scaling, or infrastructure issue.

In AWS, a Lambda function interacts with other services by using its execution role (an IAM role). AWS documentation explains that a Lambda function must have explicit IAM permissions to call downstream services such as DynamoDB. To write items, the role typically needs actions like dynamodb:PutItem (and sometimes dynamodb:UpdateItem, dynamodb:BatchWriteItem, depending on code behavior) on the target table resource ARN. If these permissions are missing or scoped incorrectly, DynamoDB returns an AccessDeniedException (or similar) and the function fails at the write step.

Option A is unlikely because exceeding concurrency would typically prevent invocation or cause throttling at the Lambda service level, not selectively fail only at DynamoDB write time after the function begins executing.

Option B is incorrect: DynamoDB does not require a GSI to support writes. GSIs are for alternate query access patterns, not mandatory for write operations.

Option D is incorrect because DynamoDB is a regional service, not tied to a single Availability Zone, and Lambda does not need to be “in the same AZ” to access it.

Therefore, the most likely cause is that the Lambda execution role lacks the necessary IAM permissions to perform DynamoDB write operations.

Distributed Tracing with X-Ray: X-Ray helps visualize request paths and identify bottlenecks in applications distributed across Regions.

Region Annotations (Automatic for AWS Services): X-Ray automatically adds a Region annotation to segments representing calls to AWS services. This aids in tracing cross-Region traffic.

Region Annotations (Manual for User-Defined): For segments representing calls to user-defined services in different Regions, the developer needs to add the Region annotation manually to enable comprehensive tracing.

Comprehensive and Detailed Step-by-Step Explanation:

Option D: Set up Amazon S3 Caching for CodeBuild:

CodeBuild supports S3 caching to store intermediate build artifacts and dependencies. This reduces the time required to download dependencies during subsequent builds, effectively lowering costs and improving build performance.

By using S3 caching, developers can optimize costs without changing the compute type or adding complexity.

Why Other Options Are Incorrect:

Option A: CodeBuild does not have a " reserved capacity fleet " option.

Option B: AWS Lambda cannot be used as the compute mode for CodeBuild projects. CodeBuild uses its own managed build environments.

Option C: CodeBuild already operates on an on-demand basis, so this does not address the need for optimization or reduced provisioning time.

“Encryption in transit” means the connection between the client (EC2 instances) and Amazon S3 must use TLS/HTTPS , not plain HTTP. The most direct way to enforce this at the bucket level is an S3 bucket policy that denies any request that is not using secure transport. AWS provides the global condition key aws:SecureTransport , which evaluates to true when a request is made over HTTPS and false when made over HTTP.

By adding an explicit Deny statement conditioned on aws:SecureTransport = false, the bucket rejects any insecure requests regardless of who makes them. This ensures that even if an application is misconfigured (or a user tries to access the bucket over HTTP), S3 will refuse the request. This is a strong enforcement mechanism because explicit denies in IAM policies override allows.

Option C (SSE-KMS) provides encryption at rest , not in transit. It protects objects while stored in S3 but does not force clients to use TLS for transport. Option B (VPC endpoint) can keep traffic on the AWS network path and can be combined with endpoint policies, but it does not inherently guarantee TLS usage unless you also enforce HTTPS. Option A (installing certificates) is not the mechanism for controlling whether the application uses HTTPS to S3; the S3 client uses AWS-managed TLS endpoints, and enforcement should happen through policy.

Therefore, the correct solution is D : create an S3 bucket policy that denies requests when aws:SecureTransport is false, ensuring all access uses encrypted transport (HTTPS/TLS).

TTL for Automatic Deletion: DynamoDB ' s Time-to-Live effortlessly deletes expired items without manual intervention.

DynamoDB Stream: Captures changes to the table, including deletions of expired items, triggering downstream actions.

Lambda for Processing: A Lambda function connected to the stream provides custom logic for handling the deleted items.

Code Efficiency: This solution leverages native DynamoDB features and stream-based processing, minimizing the need for custom code.

Step-by-Step Breakdown:

Requirement Summary:

In-transit encryption (when data is moving between client and S3)

Encryption at rest using AWS KMS keys (which are rotatable on-demand)

Option A: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using permissions boundary

Incorrect: Permissions boundaries are for IAM policy control, not S3 bucket policies.

Also, encryption enforcement in-transit is not achieved through permissions boundaries.

Option B: Configure an S3 bucket policy to enable client-side encryption

Incorrect: AWS does not enforce client-side encryption via S3 bucket policies.

Client-side encryption must be handled entirely by the application.

Also, KMS is for server-side encryption, not client-side.

Option C: Configure the application to encrypt the objects by using an AWS KMS customer managed key before uploading the objects

Correct: This fulfills the requirement of encrypting objects at rest using a KMS CMK, which can be rotated.

You can specify the KMS key using the SSE-KMS option when putting an object.

Option D: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using the aws:SecureTransport condition

Correct: This enforces in-transit encryption, which ensures that only HTTPS connections are allowed.

This is the AWS-recommended way to enforce encryption in transit via bucket policy.

Option E: Configure S3 Block Public Access settings for the S3 bucket to allow only encrypted connections over HTTPS

Incorrect: Block Public Access is used to prevent public exposure of the bucket.

It has nothing to do with encryption enforcement, whether in transit or at rest.

Using SSE-KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html

aws:SecureTransport condition in bucket policies: https://docs.aws.amazon.com/AmazonS3/latest/userguide/amazon-s3-policy-keys.html

Server-side encryption with AWS KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/serv-side-encryption.html

For in-place deployments, AWS CodeDeploy uses a set of predefined hooks that run in a specific order during each deployment lifecycle event. The hooks are ApplicationStop, BeforeInstall, AfterInstall, ApplicationStart, and ValidateService. The run order of the hooks for in-place deployments is as follows:

ApplicationStop: This hook runs first on all instances and stops the current application that is running on the instances.

BeforeInstall: This hook runs after ApplicationStop on all instances and performs any tasks required before installing the new application revision.

AfterInstall: This hook runs after BeforeInstall on all instances and performs any tasks required after installing the new application revision.

ApplicationStart: This hook runs after AfterInstall on all instances and starts the new application that has been installed on the instances.

ValidateService: This hook runs last on all instances and verifies that the new application is running properly on the instances.

The goal is near-real-time propagation of changes from one DynamoDB table (Accounts) to another service’s datastore (Payments) while keeping services decoupled. The simplest and most reliable DynamoDB-native change feed is Amazon DynamoDB Streams.

DynamoDB Streams captures item-level modifications (inserts, updates, deletes) in time order and makes them available as a stream of change records. A consumer (commonly an AWS Lambda function) can process stream records and apply the necessary updates to the Payments database/table (or publish events for downstream processing). This provides near-real-time updates with minimal operational overhead, strong integration, and a decoupled architecture because the Accounts service simply emits changes and does not need to call Payments synchronously.

Option A (Glue ETL) is batch-oriented and adds heavy operational complexity; it’s not the simplest nor near-real-time.

Option B introduces a cache as a synchronization mechanism, which complicates consistency and durability; caches are not ideal as the primary propagation channel.

Option C (Firehose) is designed for delivering streaming data to destinations like S3, Redshift, OpenSearch, etc., and is not the standard approach for DynamoDB-to-DynamoDB change replication.

Therefore, DynamoDB Streams is the best fit for near-real-time, decoupled updates between DynamoDB-backed microservices.

API Gateway Stage Variables allow you to dynamically point to different backends based on the stage. You can configure the Lambda integration to use a variable (e.g., ${stageVariables.lambdaAlias} ) instead of a static ARN. By deploying to a new " production " stage and setting the stage variable lambdaAlias to " prod " , the production stage will invoke the production version of the function while the dev stage continues to use " dev " .

The least implementation effort is to publish a custom CloudWatch metric from the existing Lambda function that records the number of orders in each batch, then create a CloudWatch alarm that sends a notification to the SNS topic when the metric value is 0. This avoids building a separate stream-processing pipeline or scheduled analytics job. A new Kinesis consumer and Lambda function would add unnecessary services and operational complexity. A 24-hour scheduled function is also slower than required because the notification must happen as soon as possible. Logging to Kinesis only creates more data movement and still requires separate analysis logic. CloudWatch supports custom metrics, and CloudWatch alarms can send notifications through Amazon SNS when metric thresholds are breached. ( AWS Documentation )

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The combination of steps that will resolve the latency issue is to create an Amazon CloudFront distribution to cache the static content and store the application’s static content in Amazon S3. This way, the company can use CloudFront to deliver the static content from edge locations that are closer to the website users, reducing latency and improving performance. The company can also use S3 to store the static content reliably and cost-effectively, and integrate it with CloudFront easily. The other options either do not address the latency issue, or are not necessary or feasible for the given scenario.

Comprehensive and Detailed 250 to 300 words of Explanation From AWS Developer Documents:

The key requirement in this scenario is that the data must be encrypted before it is uploaded to Amazon S3 , and the data is generated outside of AWS . This explicitly points to a client-side encryption requirement.

AWS documentation distinguishes clearly between server-side encryption and client-side encryption . Server-side encryption options for Amazon S3—including enabling default bucket encryption or specifying the x-amz-server-side-encryption request header—encrypt the data after it is received by Amazon S3. Therefore, options B and D do not satisfy the requirement because the plaintext data is transmitted to AWS before encryption occurs.

Option A, using the AWS KMS encrypt command, is not suitable for large binary objects. AWS KMS is designed to encrypt small pieces of data (up to 4 KB). AWS documentation explicitly states that for large payloads, customers should use envelope encryption instead of directly encrypting data with AWS KMS.

The AWS Encryption SDK is specifically designed for client-side encryption of large data objects. It implements envelope encryption , where a data encryption key (DEK) is generated locally to encrypt the data, and the DEK is then encrypted with an AWS KMS key. This approach allows efficient encryption of large binary files while maintaining strong key management and security controls.

AWS documentation recommends the AWS Encryption SDK for applications that need to encrypt data before sending it to AWS services , including Amazon S3. It supports multiple programming languages, handles key management automatically, and ensures data confidentiality even before transmission.

Therefore, using the AWS Encryption SDK for client-side encryption is the only solution that fully satisfies all requirements.

AWS Amplify is a set of tools and services that enables developers to build and deploy full-stack web and mobile applications that are powered by AWS. AWS Amplify supports hosting static websites on Amazon S3 and Amazon CloudFront, with HTTPS enabled by default. AWS Amplify also integrates with various version control systems, such as AWS CodeCommit, Bitbucket, and GitHub, and allows developers to connect different branches to different environments. AWS Amplify automatically builds and deploys the website whenever code changes are merged to a connected branch, enabling phased releases with minimal operational overhead. Reference: AWS Amplify Console

Amazon Kinesis Data Firehose is a service that delivers real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Amazon Kinesis Data Analytics. The developer can implement Kinesis Data Firehose data transformation as an AWS Lambda function. The function can remove pattern-based customer identifiers from the data and return the modified data to Kinesis Data Firehose. The developer can set an Amazon S3 bucket as the destination of the delivery stream.

AWS CodeCommit is a service that provides fully managed source control for hosting secure and scalable private Git repositories. The development team can use CodeCommit to store the program code and prepare for the CI/CD pipeline. CodeCommit integrates with other AWS services such as CodePipeline, CodeBuild, and CodeDeploy to automate the code build and deployment process.

EventBridge (formerly CloudWatch Events) is the ideal service for real-time event monitoring.

CloudTrail logs IAM role creation.

EventBridge rules can filter CloudTrail events and trigger SNS notifications instantly.

The combination of steps that the developer should take to fix the errors is to deploy AWS X-Ray as a sidecar container to the microservices and instrument the ECS task to send the stdout and stderr output to Amazon CloudWatch Logs. This way, the developer can use AWS X-Ray to analyze and debug the performance of the microservices and identify any issues or bottlenecks. The developer can also use CloudWatch Logs to monitor and troubleshoot the logs from the ECS task and detect any errors or exceptions. The other options either involve using AWS X-Ray as a daemon set, which is not supported by Fargate, or using the PutTraceSegments API call, which is not necessary when using a sidecar container.

Amazon CloudWatch Logs Insights is a purpose-built log analytics tool that allows developers to run interactive queries directly against CloudWatch Logs. AWS documentation highlights Logs Insights as the fastest and most efficient way to search, filter, and analyze log data without exporting it.

Logs Insights queries can filter error messages, extract timestamps, and aggregate failure counts over specific time windows. This directly satisfies the requirement to identify when intermittent failures occurred and what errors caused them.

Manual browsing (Option A) and local downloads (Option C) are time-consuming and do not scale. Exporting logs to S3 and querying with Athena (Option D) introduces unnecessary infrastructure and operational overhead.

Therefore, CloudWatch Logs Insights is the most operationally efficient solution.

The most secure and AWS-recommended way for a Lambda function to access other AWS services is to use an IAM execution role. Lambda assumes this role at runtime and receives temporary credentials that are automatically rotated by AWS. The developer attaches the necessary permissions (the existing IAM policy) to that role, and then configures the Lambda function to use the role.

Option B follows least privilege and avoids long-term credentials. It also integrates with AWS security tooling: IAM Access Analyzer, CloudTrail, and policy boundaries can all be applied cleanly. Because the policy already exists, this requires minimal extra work: create/choose the execution role, attach the policy, and assign the role to the function.

Option A is not correct because IAM policies are attached to IAM identities (users, groups, roles) — not directly to Lambda functions as standalone entities. Lambda permissions are granted through the function’s execution role.

Option C is insecure because it uses long-term IAM user access keys embedded in environment variables. Even if encrypted, this expands the blast radius, complicates rotation, and contradicts AWS best practices for avoiding static credentials inside code/runtime.

Option D is extremely insecure and noncompliant. Root user access keys should not be used for applications and should generally not exist.

Therefore, create and attach the existing policy to a Lambda execution IAM role and assign that role to the function.

Using Versions and Aliases is the native AWS Lambda mechanism for managing deployments. A version is an immutable snapshot of your code and configuration. An alias (like " PROD " ) is a pointer to a specific version. By updating the alias to point to a new version, you promote code. If a bug is found, you simply point the alias back to the previous version, enabling an instantaneous rollback with minimal overhead.

Why Option C is Correct:Ensuring that pipeline artifacts are encrypted with a customer-managed AWS KMS key involves configuring the S3 bucket policy to require encryption. This policy ensures all objects uploaded to the bucket are encrypted with the specified KMS key.

Why Other Options are Incorrect:

Option A: A gateway endpoint improves S3 access efficiency but does not enforce encryption.

Option B: Encrypting CloudWatch Logs is unrelated to securing pipeline artifacts.

Option D: Configuring SNS for encryption does not affect the artifacts stored in the S3 bucket.

AWS Documentation References:

Using Server-Side Encryption with S3 Bucket Policies

The requirement is real-time UI updates “immediately” without refreshing the dashboard. The most operationally efficient AWS-native method is to use API Gateway WebSocket APIs to push updates from the backend to the browser.

With option A, the client establishes a WebSocket connection and receives a connection ID. The application can associate uploads (or job IDs) with that connection ID (commonly storing the mapping in DynamoDB or another datastore). When the long-running validation finishes, the backend uses the WebSocket management API to post a message to the specific connection ID, and the browser updates the UI dynamically. This eliminates polling/refresh and scales well without managing servers.

Option B requires running and maintaining an EC2-hosted WebSocket server (patching, scaling, uptime), which is more operational overhead.

Option C uses email, which is not immediate UI updating and doesn’t update the dashboard without user action.

Option D is not appropriate: SNS does not directly push notifications to a web browser in a standard way, and it adds unnecessary complexity.

Therefore, using API Gateway WebSocket to push validation results to connected clients is the most operationally efficient solution.

API Gateway Mocking: This feature is built for decoupling development dependencies. Here ' s the process:

Create resources and methods in your API Gateway.

Set the integration type to ' MOCK ' .

Define Integration Responses, mapping HTTP status codes to desired mocked responses (JSON, etc.).

Deployment and Use:

Create a deployment stage for the API.

Frontend teams can call this API and get the mocked responses without a real backend.

The business rule requires controlled sequencing: inventory must determine which order gets the seat before payment is processed. Running inventory and payment in parallel is unsafe because the payment Lambda could charge an order that later loses the seat. A standard SNS topic or a shared SQS queue does not enforce the required business sequence between inventory reservation and payment processing. An SNS FIFO fanout can preserve message order to SQS FIFO queues, but it still sends events to separate inventory and payment workflows, which does not guarantee that payment waits for inventory decision and fallback logic. Initiating inventory first and payment second is the only option that directly enforces the required order. For a production-grade design, Step Functions would be even cleaner, but it is not offered.

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AWS SAM CLI is the correct tool for local Lambda testing with realistic event payloads. The developer can use sam local generate-event s3 put to create a sample S3 PutObject event and then pass that event to sam local invoke to run the Lambda function locally. This avoids deploying to production or manually constructing event JSON. Uploading an object to S3 will invoke a Lambda function configured in AWS, not a local function. The aws lambda invoke command invokes a deployed Lambda function, not a local one. The AWS Management Console also tests deployed functions, not local code. AWS SAM documentation confirms that sam local generate-event creates supported AWS service event payloads and sam local invoke invokes Lambda functions locally. ( AWS Documentation )

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The solution that will meet the requirements with the least development effort is to set the image resize Lambda function as a destination of the avatar generator Lambda function for the events that fail processing. This way, the fallback mechanism is automatically triggered by the Lambda service without requiring any additional components or configuration. The other options involve creating and managing additional resources such as queues, topics, state machines, or rules, which would increase the complexity and cost of the solution.

Why Option A is Correct:Auto-scaling with a target utilization ensures the DynamoDB table dynamically adjusts capacity based on workload, maintaining high performance while optimizing cost. Setting a reasonable target utilization minimizes overprovisioning and throttling risks.

Why Other Options are Incorrect:

Option B: On-demand capacity is costlier than provisioned throughput for predictable workloads.

Option C: Using manual CloudWatch alarms and Lambda for scaling is less efficient and adds operational overhead.

Option D: DAX accelerates read performance but does not improve write performance.

AWS Documentation References:

DynamoDB Auto Scaling

A function alias is a pointer to a specific Lambda function version. You can use aliases to create different environments for your function, such as development, testing, and production. You can also use aliases to perform blue/green deployments by shifting traffic between two versions of your function gradually. This way, you can easily roll back to a previous version if something goes wrong, without having to redeploy your code or change your configuration. Reference: AWS Lambda function aliases

AWS Lambda allocates CPU, network throughput, and other resources proportionally to the function’s configured memory . If a Lambda function is CPU-bound (common for image manipulation such as resizing and thumbnail generation) or limited by I/O throughput, increasing the configured memory often improves performance because it also increases the amount of CPU available to the function. This is a standard Lambda optimization technique: tune memory to reduce runtime and achieve a lower latency target.

In this scenario, the thumbnail generation is taking more than 2 minutes, but the requirement is to process each image in under 30 seconds. Without changing the code, the most direct lever is to increase the function’s memory setting and retest. As memory increases, Lambda provides more compute power, which can significantly speed up CPU-intensive libraries (for example, image decoding, resizing, encoding) and reduce overall execution time. Many workloads show near-linear improvements until other bottlenecks (such as downstream service latency) dominate.

Options A and B are not applicable to Lambda in this way. Lambda does not let you directly select a fixed number of vCPUs or choose an EC2 instance type such as m6a.4xlarge for a function; those are EC2 concepts, not Lambda configuration parameters. Option D (“burstable performance”) is also not a Lambda configuration model; burstable performance applies to certain EC2 instance families, not Lambda’s managed execution environment.

Therefore, the correct action is C : increase the Lambda function’s memory allocation (which also increases available CPU and throughput). This is the AWS-supported way to improve performance for compute-intensive Lambda functions and is the most likely to bring processing time down toward the 30-second requirement without code changes.

The most operationally efficient way to remove time-bound data from a DynamoDB table is to use Time to Live (TTL) . TTL is a DynamoDB feature that lets you define an attribute (typically a Unix epoch timestamp) that represents an item’s expiration time. After the timestamp has passed, DynamoDB automatically marks the item as expired and later deletes it in the background. This directly satisfies the requirement because the telemetry is only valuable for a limited time, yet it is currently stored indefinitely.

Enabling TTL (option C) improves performance and operational efficiency by preventing the table from growing without bounds. As the item count grows, access patterns that rely on scans, large partitions, or indexes can degrade, and storage-related overhead increases. TTL helps keep the dataset “fresh” by automatically removing stale telemetry, reducing the amount of data the application must work around and decreasing overall storage footprint.

Option A is operationally heavier: scanning and deleting items with a scheduled Lambda introduces ongoing maintenance, costs, and risk of throttling (table scans can be expensive and disruptive). It also requires custom logic, error handling, and retries. Option B addresses cost optimization for archived data but does not fix the DynamoDB table size or the performance degradation caused by keeping old data in the table; archiving is useful, but you still need an efficient deletion mechanism from DynamoDB. Option D (on-demand capacity mode) changes how capacity is managed, not how much data is stored; it does not remove stale items and therefore does not address the core problem.

Therefore, the best and most operationally efficient solution is C : add a TTL attribute to each telemetry item and enable TTL on the DynamoDB table so DynamoDB automatically expires and deletes old telemetry.

When data access is skewed toward a specific date (today), using ForecastDate as the partition key results in a " hot partition. " To resolve this, " Write Sharding " or " Partition Key Sharding " is used. Adding a calculated suffix (like a random or calculated number 1..N ) to the partition key distributes the requests for a single date across multiple physical partitions. CityId is then used as the sort key to keep city-specific data organized within those partitions.

Global Secondary Index (GSI): GSIs enable alternative query patterns on a DynamoDB table by using different partition and sort keys.

Addressing Query Bottleneck: By making the slow-query attribute the GSI ' s partition key, you optimize queries on that attribute.

Scalability: GSIs automatically scale to handle increasing data volumes.

DynamoDB Streams: Capture near real-time changes to DynamoDB tables, triggering downstream actions.

Lambda for Processing: Lambda functions provide a serverless way to execute code in response to events like DynamoDB Stream updates.

Minimal Code Changes: This solution requires the least modifications to the existing application.

On Amazon ECS, the fundamental scheduling unit is a task , which is launched from a task definition . A single task definition can include multiple containers that are intended to run together (often called a “sidecar” pattern). When two containers must share data locally —such as an application container writing log files and a separate log/metrics collector container reading those files—the recommended approach is to place both containers in the same task definition and use a shared task volume .

ECS supports defining volumes at the task level (for example, a Docker volume or bind mount depending on the launch type and configuration), and then mounting that volume into multiple containers inside the same task. Because the containers are co-located by ECS scheduling (same task, same underlying host or Fargate environment), they can safely share the mounted directory for file-based handoff. This design keeps the containers tightly coupled for lifecycle management (start/stop together) and avoids cross-task coordination complexity.

Options A and D refer to “pod specifications,” which are Kubernetes concepts, not ECS-native constructs (unless using ECS with EKS/Kubernetes, which is not stated). Option B is not feasible because ECS does not mount a single shared local volume across two separate tasks in the general case; separate tasks can be placed on different hosts, and task-scoped volumes are not shared between tasks by default. If cross-task sharing were required, you would typically use a network filesystem (like EFS) or an external log pipeline (CloudWatch Logs, FireLens, etc.), but the requirement explicitly says each application container needs to share data with another container, which aligns with the sidecar-in-same-task pattern.

Therefore, the correct solution is C : define both containers in one ECS task definition and mount a shared volume into both containers.

The safest way to prevent cross-tenant data leakage during processing is to enforce tenant isolation at the authorization layer , not only in application logic. AWS supports this with ABAC (attribute-based access control) using IAM principal/session tags and policy condition keys. The goal is to ensure that, for each invocation, the function can access only the S3 prefix and DynamoDB items that match the tenant being processed.

First, the Lambda execution role should be permitted to assume a dedicated data access role (B). This creates a clear separation: the execution role has minimal base permissions and can obtain tenant-scoped permissions only by assuming the data role.

Second, the Lambda function should assume that data access role with the tenant name as a session tag and then use the temporary credentials for all S3/DynamoDB calls (F). Session tagging ties the caller’s identity attributes (tenant) to the credentials used for data access.

Third, attach policies to the data access role that restrict access using conditions that compare the session tag to the requested resource attributes (C). For S3, policies can restrict access to object ARNs like arn:aws:s3:::bucket/${aws:PrincipalTag/tenant}/*. For DynamoDB, policies can restrict access by partition key using dynamodb:LeadingKeys so the role can read/write only items whose partition key equals the tenant tag. This ensures that even if the function code has a bug or receives unexpected input, AWS authorization prevents it from reading or writing another tenant’s data.

Option A is not required as stated; you typically need permission to pass session tags (and the trust/policy must allow tagging), but the key actions here are: enable assume role (B), enforce tag-based data policies (C), and actually assume the role with tenant tag (F). Option D is not generally the primary control for DynamoDB; the standard enforcement is via IAM identity policies (and dynamodb:LeadingKeys). Option E (RCP) is an AWS Organizations guardrail and is not necessary for this single-application isolation pattern.

Why Option B is Correct:

Amazon S3 Standard provides immediate access to files and is cost-effective for files that need to be accessed within 5 minutes.

By adding the S3 location to the SQS queue, you avoid transferring large files directly, which is both more efficient and scalable.

Why Other Options are Incorrect:

Option A: S3 Glacier Deep Archive is designed for archival storage with retrieval times ranging from minutes to hours, which does not meet the 5-minute requirement.

Option C: Amazon EBS is designed for block storage attached to EC2 instances, which adds unnecessary complexity and cost.

Option D: SQS is not designed to handle large file content directly and has message size limits (256 KB).

AWS Documentation References:

Amazon S3 Overview

Amazon SQS Best Practices

The requirement is specifically encryption at rest for messages stored in Amazon SQS. The AWS-native way to accomplish this is to enable server-side encryption (SSE) on the SQS queue . When SSE is enabled, SQS encrypts messages as they are stored and decrypts them when they are retrieved, using keys from AWS Key Management Service (AWS KMS) (either an AWS managed key for SQS or a customer managed KMS key). This provides transparent encryption at rest without requiring application-level cryptography changes.

Option C correctly states to enable SSE on the queue. Once enabled, any message content placed in the message body will be protected at rest in SQS. (Message attributes are also stored by SQS; however, the key requirement is to ensure the messages are encrypted at rest, and enabling SSE at the queue level is the control that enforces this.)

Option A (aws:SecureTransport) enforces encryption in transit by requiring HTTPS/TLS, not encryption at rest. It is a good security control, but it does not satisfy the at-rest requirement by itself. Option B is incorrect because SSE is not something the sender “sets within the message”; SSE is configured on the queue, not per-message by the microservices. Option D suggests putting sensitive data in attributes, which does not add security; it can actually increase exposure because attributes are often used for routing/filters and may be logged or surfaced in monitoring. The right pattern is to keep sensitive data where intended and rely on queue-level SSE to encrypt stored messages.

Therefore, enabling SQS SSE on the queue (C) meets the requirement to ensure messages are encrypted at rest while stored in SQS.

The company already has SSM Agent deployed on all servers, which enables centralized, scalable fleet operations without logging in to each instance. The most operationally efficient solution is to automate key distribution using Systems Manager Run Command, executing a script across the entire fleet (or a targeted subset) in one action.

Option B uses S3 as a simple distribution source and Run Command to perform the update at scale. The developer can store the required key material in a controlled S3 location and write a script that places the key in the correct filesystem path (with correct permissions/ownership), updates authorized_keys if needed, and restarts any services if required. Run Command can target instances by tags, resource groups, or instance IDs and provides execution logging and status.

Option A is not operationally efficient because it requires logging into each server manually.

Option C and D push the distribution burden to humans (“grant team members permissions to download”), which is error-prone, slow, and not scalable. Also, distributing private keys broadly to individuals increases risk.

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/access-control-overview.html#access-control-resource-ownership

Because the developer cannot change the legacy application code, the solution must apply controls at the logging layer . CloudWatch Logs provides data protection policies that can automatically detect sensitive data using managed data identifiers (including credit card data) and apply masking (de-identification) as log events are ingested. This ensures that sensitive values such as card numbers and verification codes are not displayed in plaintext to general viewers of the log group.

With option A, enabling a data protection policy on the log group and configuring it to mask the relevant credit card data ensures that when support staff query or view logs, the sensitive fields appear redacted. CloudWatch Logs also supports controlled access to view the original unmasked values through an explicit permission: users who have the appropriate IAM authorization (such as logs:Unmask) can retrieve or view the sensitive data, while everyone else sees only the masked version. Granting logs:Unmask to application administrators satisfies the requirement that administrators can access the sensitive data, while withholding it from support staff enforces the restriction.

Option B (KMS encryption) provides encryption at rest for the log group but does not prevent authorized readers from seeing sensitive data once decrypted. Support staff who can read logs would still see plaintext card data. Option C is not a standard or operationally efficient pattern for CloudWatch Logs redaction; Macie is primarily for discovering sensitive data in S3 and does not serve as the native masking mechanism for CloudWatch Logs ingestion. Option D is not applicable: AWS WAF inspects HTTP requests/responses at the edge or load balancer/API layer, not application log streams already being emitted to CloudWatch.

Therefore, A is the correct solution: use CloudWatch Logs data protection to mask sensitive data by default and grant logs:Unmask only to administrators.

To give the frontend team immediate access to stable endpoints without waiting for a real backend implementation, the simplest approach is to use API Gateway MOCK integration . A MOCK integration lets API Gateway generate a response directly, without invoking Lambda or any other backend. This is ideal for early UI development because you can return consistent, predefined payloads and status codes for each method.

With MOCK integration, you configure an integration request (often using a static mapping template) to produce a dummy request payload that triggers a response, and then you configure integration responses to return specific HTTP status codes (such as 200, 400, 500) along with predefined JSON bodies. You can also set response headers (for example, Content-Type: application/json) and use mapping templates to shape the returned JSON. This satisfies the requirement to “return predefined HTTP status codes and JSON responses” while minimizing development effort and avoiding the need to deploy placeholder Lambda functions.

Option A (AWS_PROXY with Lambda) can work, but it requires creating and deploying Lambda functions and IAM permissions just to return static data—more effort than necessary. Option C (HTTP PROXY) depends on an external placeholder service, which adds another system to build and maintain. Option D is incorrect because HTTP status codes are not defined in the method request ; they are defined through method responses and realized through integration responses in API Gateway’s integration configuration.

Therefore, B is the best solution: MOCK integration with integration request/response mappings provides quick, code-free stubs that unblock frontend development.