Amazon Web Services DVA-C02 AWS Certified Developer - Associate Exam Practice Test

Problem: CloudFormation can't find the custom AMI in the target region (us-west-1) because AMIs are region-specific.

Copying AMIs:

AMIs can be copied across regions, maintaining their configuration.

This approach minimizes operational overhead as the existing CloudFormation template can be reused with a minor update.

Updating the Template:

Modify the CloudFormation template in us-west-1 to reference the newly copied AMI's ID in that region.

References:

Copying AMIs: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/CopyingAMIs.html

CloudFormation Templates and AMIs: [invalid URL removed]

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 \

--s3-bucket \

--s3-prefix \

--export-time \

--region

References:

Exporting DynamoDB Data to Amazon S3

S3 Bucket Region Requirements for DynamoDB Exports

AWS CLI Reference for DynamoDB Export

The immutable deployment method is the best option for this scenario, because it meets the requirements of maintaining full capacity, avoiding service interruption, and minimizing the cost of additional resources.

The immutable deployment method creates a new set of instances in a separate Auto Scaling group and deploys the new version of the application to them. Then, it swaps the new instances with the old ones and terminates the old instances. This way, the application maintains full capacity during the deployment and avoids any downtime. The cost of additional resources is also minimized, because the new instances are only created for a short time and then replaced by the old ones.

The other deployment methods do not meet all the requirements:

The all at once method deploys the new version to all instances simultaneously, which causes a short period of downtime and reduced capacity.

The rolling with additional batch method deploys the new version in batches, but for the first batch it creates new instances instead of using the existing ones. This increases the cost of additional resources and reduces the capacity of the original environment.

The blue/green method creates a new environment with a new set of instances and deploys the new version to them. Then, it swaps the URLs between the old and new environments. This method maintains full capacity and avoids service interruption, but it also increases the cost of additional resources significantly, because it duplicates the entire environment.

The sam local start-api subcommand allows you to run your serverless application locally for quick development and testing1. It creates a local HTTP server that acts as a proxy for API Gateway and invokes your Lambda functions based on the AWS SAM template1. You can use the sam local start-api subcommand to test your REST API locally by sending HTTP requests to the local endpoint1.

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

In the CloudFormation template, the developer should create a parameter with the list of approved EC2 instance types as AllowedValues. This way, users can select the instance type they want to use when launching the CloudFormation stack, but only from the approved list.

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:To redact PII from S3 objects before they are accessed by the analytics service, the most efficient solution is to use S3 Object Lambda. S3 Object Lambda allows you to add your own code (Lambda function) to process and transform data when it is retrieved from Amazon S3. You can attach a Lambda function to an S3 Object Lambda Access Point, which in this case would run a redaction API to remove PII from the reports.

Operational Efficiency: S3 Object Lambda handles data processing on the fly, without requiring the data to be permanently transformed or moved to another service (like Amazon Redshift).

Alternatives:

Option A: Loading the data into Amazon Redshift would require refactoring the analytics service and maintaining an additional data pipeline, increasing complexity.

Option C: Using AWS KMS for encryption protects data at rest and in transit, but it does not address PII redaction.

Option D: SNS is a messaging service and does not support direct data transformation.

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.

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.

The amount of memory you allocate to your Lambda function also determines how much CPU and network bandwidth it gets. Increasing the memory size can improve the performance of CPU-bound functions by giving them more CPU power. The CPU allocation is proportional to the memory allocation, so a function with 1 GB of memory has twice the CPU power of a function with 512 MB of memory. Reference: AWS Lambda execution environment

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:The AWS Serverless Application Model (SAM) includes features for local testing and debugging of AWS Lambda functions. One of the most efficient ways to generate test payloads that match actual AWS event structures is by using the sam local generate-event command.

sam local generate-event: This command allows developers to create pre-configured test event payloads for various AWS services (e.g., S3, API Gateway, SNS). These generated events accurately reflect the format that the service would use in a live environment, reducing the manual work required to create these events from scratch.

Operational Overhead: This approach reduces overhead since the developer does not need to manually create or maintain test events. It ensures that the structure is correct and up-to-date with the latest AWS standards.

Alternatives:

Option A suggests using shareable test events, but manually creating or sharing these events introduces more overhead.

Option B and C both involve manually storing and maintaining test events, which adds unnecessary complexity compared to using sam local generate-event.

IAM instance profiles are containers for IAM roles that can be associated with EC2 instances. An IAM role is a set of permissions that grant access to AWS resources. An IAM role can be used to allow an EC2 instance to access an S3 bucket by including the appropriate permissions in the role’s policy. The S3:ListBucket permission allows listing the objects in an S3 bucket. By updating the IAM instance profile with this permission, the application on the EC2 instance can retrieve the objects from the S3 bucket and display them to the user. Reference: Using an IAM role to grant permissions to applications running on Amazon EC2 instances

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

Amazon ElastiCache for Redis: An in-memory data store known for extremely low latency, ideal for caching frequently accessed, complex data.

Write-Through Caching: Ensures that data is always consistent between the cache and the database. Writes go to both Redis and RDS.

Performance Gains: Redis handles reads with minimal latency, offloading the RDS database and improving the application's responsiveness.

References:

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

Caching Strategies: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/Strategies.html

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.

References:

Docker: https://www.docker.com/

Amazon ECR: https://aws.amazon.com/ecr/

This solution will meet the requirements by using Amazon RDS Proxy, which is a fully managed, highly available database proxy for Amazon RDS that makes applications more scalable, more resilient to database failures, and more secure. The developer can create a proxy in Amazon RDS Proxy, which sits between the application and the DB instance and handles connection management, pooling, and routing. The developer can query the proxy instead of the DB instance, which reduces the number of open connections to the DB instance and avoids errors for too many connections. Option A is not optimal because it will create a read replica for the DB instance, which may not solve the problem of too many connections as read replicas also have connection limits and may incur additional costs. Option B is not optimal because it will migrate the data to an Amazon DynamoDB database, which may introduce additional complexity and overhead for migrating and accessing data from a different database service. Option C is not optimal because it will configure the Amazon Aurora MySQL DB instance for Multi-AZ deployment, which may improve availability and durability of the DB instance but not reduce the number of connections.

References: [Amazon RDS Proxy], [Working with Amazon RDS Proxy]

A Lambda alias is a pointer to a specific Lambda function version or another alias1. A Lambda alias allows you to invoke different versions of a function using the same name1. You can also split traffic between two aliases by assigning weights to them1.

In this scenario, the developer needs to test their changes in production on a small percentage of all traffic without changing the API Gateway URL. To achieve this, the developer can follow these steps:

Define an alias on the $LATEST version of the Lambda function. This will create a new alias that points to the latest code of the function.

Update the API Gateway endpoint to reference the new Lambda function alias. This will make the API Gateway invoke the alias instead of a specific version of the function.

Upload and publish the optimized Lambda function code. This will update the $LATEST version of the function with the new code.

On the production API Gateway stage, define a canary release and set the percentage of traffic to direct to the canary release. This will enable API Gateway to perform a canary deployment on a new API2. A canary deployment is a software development strategy in which a new version of an API is deployed for testing purposes, and the base version remains deployed as a production release for normal operations on the same stage2. The canary release receives a small percentage of API traffic and the production release takes up the rest2.

Update the API Gateway endpoint to use the $LATEST version of the Lambda function. This will make the canary release invoke the latest code of the function, which contains the optimized changes.

Publish to the canary stage. This will deploy the changes to a subset of users for testing.

By using this solution, the developer can test their changes in production on a small percentage of all traffic without changing the API Gateway URL. The developer can also monitor and compare metrics between the canary and production releases, and promote or disable the canary as needed2.

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_examples_lambda-access-dynamodb.html

API Gateway Stages: Stages in API Gateway represent distinct environments (like PROD and DEV) allowing different configurations.

Stage Variables: Stage variables store environment-specific information, including Lambda function aliases.

Ease of Management: This solution offers a straightforward way to manage different Lambda function versions across environments.

References:

API Gateway Stages: https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-stages.html

API Gateway Stage Variables: https://docs.aws.amazon.com/apigateway/latest/developerguide/stage-variables.html

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:When you need to provide an AWS Lambda function access to private resources in a VPC, the most common and straightforward approach is to attach the Lambda function to a VPC via private subnets. Once the Lambda function is associated with the VPC, you need to configure appropriate security groups to control the access to the private resources.

Lambda with VPC Access: Lambda functions can be attached to private subnets in a VPC, allowing them to access resources like RDS, EC2, or internal services within that VPC.

Security Groups: A security group acts as a virtual firewall for the Lambda function, ensuring that it can access only the necessary resources and ports in the VPC.

Alternatives:

Option B involves routing traffic through a VPN, which adds unnecessary complexity and operational overhead compared to simply attaching the Lambda to the VPC.

Option C requires configuring a VPC endpoint and a NAT gateway, which can be complex and costly.

Option D refers to AWS PrivateLink, which is used to access services over private connections, but it's unnecessary in this scenario unless you need a cross-VPC connection.

This solution will meet the requirements by allowing the Lambda functions to access the DB cluster securely within the same VPC without crossing the public internet. The developer can configure a VPC endpoint for RDS in a private subnet and assign it to the Lambda functions. The developer can also configure a security group for the Lambda functions that allows inbound traffic from the DB cluster on port 3306 (MySQL). Option A is not optimal because it will expose the DB cluster to public access, which may compromise its security and data integrity. Option B is not optimal because it will introduce additional latency and complexity to use an RDS database proxy for accessing the DB cluster from Lambda functions within the same VPC. Option C is not optimal because it will require additional costs and configuration to use a NAT gateway for accessing resources in private subnets from Lambda functions.

References: [Configuring a Lambda Function to Access Resources in a VPC]

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.

Amazon S3 is a service that provides highly scalable, durable, and secure object storage. The developer can create an S3 bucket to host the repository and migrate the existing .xml files to the S3 bucket. The developer can update the application code to use the AWS SDK to read and write configuration files from S3. This solution will meet the requirement of high availability for the repository in a cost-effective way.

References:

[Amazon Simple Storage Service (S3)]

[Using AWS SDKs with Amazon S3]

This solution will meet the requirements by allowing the developer to control what data is sent to X-Ray and CloudWatch from the application code. The developer can filter out any PII from the trace messages before sending them to X-Ray and CloudWatch, ensuring that no PII goes outside of the EC2 instances. Option B is not optimal because it will automatically instrument all incoming and outgoing requests from the application, which may include PII in the trace messages. Option C is not optimal because it will require additional services and costs to use Amazon Macie and AWS Lambda, which may not be able to detect and hide all PII from the trace messages. Option D is not optimal because it will use Open Telemetry instead of X-Ray, which may not be compatible with CloudWatch and other AWS services.

References: [AWS X-Ray SDKs]

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

Amazon S3 supports server-side encryption, which encrypts data at rest on the server that stores the data. One of the encryption options is SSE-S3, which uses keys managed by S3. To use SSE-S3, the x-amz-server-side-encryption header must be set to AES256 when invoking the PutObject API operation. This instructs S3 to encrypt the object data with SSE-S3 before saving it on disks in its data centers and decrypt it when it is downloaded. Reference: Protecting data using server-side encryption with Amazon S3-managed encryption keys (SSE-S3)

Why Option B is Correct:A customer-managed AWS Key Management Service (KMS) key allows for encryption at rest and provides the ability to rotate the key on demand. This ensures compliance with security requirements for key management and database encryption.

RDS integrates natively with AWS KMS, allowing the use of a customer-managed key for encrypting data at rest.

Key rotation can be managed directly in AWS KMS without needing custom solutions.

Why Other Options are Incorrect:

Option A: AWS KMS managed encryption keys (AWS-owned keys) do not support key rotation on demand.

Option C & D: Storing keys in AWS Secrets Manager with custom rotation is not a recommended approach for database encryption. AWS KMS is designed specifically for secure key management and encryption.

AWS Documentation References:

Encrypting Amazon RDS Resources

AWS Key Management Service (KMS)

Why Option A is Correct:Encrypting PII at rest and in transit before storing it in DynamoDB ensures end-to-end security. Using the AWS Database Encryption SDK with KMS keys allows the Lambda function to encrypt data before transmission, meeting security and compliance requirements.

Why Other Options are Incorrect:

Option B: While AWS-managed KMS keys encrypt DynamoDB data at rest, they do not encrypt data in transit.

Option C: DynamoDB streams process updates after the data is written to the table, failing to encrypt PII in transit.

Option D: Step Functions and SQS add unnecessary complexity and still require encryption logic for both transit and at rest.

AWS Documentation References:

Encrypting Data in DynamoDB

AWS Database Encryption SDK

Comprehensive and Detailed Step-by-Step Explanation:

Option A: Use Amazon S3 for Shared Test Events:

Storing JSON test event files in an S3 bucket provides a centralized, cost-effective, and highly available solution.

Granular IAM policies can restrict access to specific developers or roles, ensuring security while maintaining consistency for shared test events.

This solution has minimal operational overhead and integrates easily with existing workflows.

Why Other Options Are Incorrect:

Option B: Using DynamoDB and a Lambda function introduces unnecessary complexity for a relatively simple requirement. S3 provides a simpler and more cost-efficient solution.

Option C: AWS Lambda test events are not inherently shareable across developers, making this option invalid.

Option D: Using a Git repository adds operational overhead and requires developers to clone/update repositories for access, which is more cumbersome compared to S3.

References:

Amazon S3 for Centralized Storage

The solution that will meet the requirements most cost-effectively is to store the smart meter readings in an Amazon DynamoDB table. Create a composite key by using the location ID and timestamp columns. Use the columns to filter on the customers’ data. This way, the company can leverage the scalability, performance, and low latency of DynamoDB to store and retrieve the smart meter readings. The company can also use the composite key to query the data by location ID and timestamp efficiently. The other options either involve more expensive or less scalable services, or do not provide low-latency access to the current usage.

Amazon EFS: A network file system (NFS) providing shared, scalable storage across multiple Lambda functions and other AWS resources.

Lambda Mounting: EFS file systems can be mounted within Lambda functions to access a shared storage space.

Log Appending: EFS supports appending data to existing files, making it ideal for the log file scenario.

References:

Amazon EFS Documentation: https://docs.aws.amazon.com/efs/

Using Amazon EFS with AWS Lambda: https://docs.aws.amazon.com/lambda/latest/dg/services-efs.html

API Gateway’s canary deployments allow gradual traffic shifting to a new version of a function, minimizing disruption while testing.

Why Option A is Correct:

Gradual Rollout: Reduces risk by incrementally increasing traffic.

Rollback Support: Canary deployments make it easy to revert to the previous version.

Why Not Other Options:

Option B: Redeploying the stage disrupts all users.

Option C & D: Managing new stages and weighted routing introduces unnecessary complexity.

References:

Canary Deployments in API Gateway

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.

References:

Mocking API Gateway APIs: https://docs.aws.amazon.com/apigateway/latest/developerguide/how-to-mock-integration.html

Step 1: Understanding the Requirements

Gradual Traffic Shift: Test the new version by routing only 10% of production traffic to it.

Lambda Deployment: Use versioning and aliases to manage Lambda function updates.

Step 2: Solution Analysis

Option A:

Publishing a new version creates an immutable version of the Lambda function with the updated code.

This is a prerequisite for deploying changes using weighted aliases.

Correct option.

Option B:

API Gateway stages are not used for weighted routing; they represent environments like "dev" or "prod."

Weighted routing is implemented using Lambda aliases, not API Gateway stages.

Not suitable.

Option C:

Lambda aliases allow traffic to be split between versions using weighted routing.

Assign a 90% weight to the old version and 10% to the new version to implement the gradual rollout.

Correct option.

Option D:

Network Load Balancers are not suitable for managing Lambda function traffic directly.

Not applicable.

Option E:

Route 53 routing policies apply at the DNS level and are not designed for Lambda version management.

Not suitable.

Step 3: Implementation Steps

Publish a New Version:

Publish the updated Lambda function code as a new version.

Create an Alias and Configure Weighted Routing:

Create an alias (e.g., prod) and associate it with both the old and new versions.

Set weights for traffic distribution:

aws lambda update-alias --function-name my-function \

--name prod --routing-config '{"AdditionalVersionWeights": {"2": 0.1}}'

AWS Developer References:

Lambda Function Aliases

Weighted Traffic Routing with Lambda

The solution that will meet the requirements is to add a second cache behavior to the distribution with the same origin as the default cache behavior. Set the path pattern for the second cache behavior to the path of the login page, and make viewer access unrestricted. Keep the default cache behavior’s settings unchanged. This way, the login page can be accessed without authentication, while all other content remains secure and requires signed cookies. The other options either do not allow unauthenticated access to the login page, or expose private content to unauthorized users.

Why Option B is Correct:Setting the visibility timeout ensures that once a message is being processed, it is temporarily hidden from other consumers. The Lambda function must delete processed messages to avoid re-processing when the visibility timeout expires.

Why Other Options are Incorrect:

Option A: Message retention affects how long messages stay in the queue, not how they are processed.

Option C: Receive message wait time optimizes long polling but does not prevent re-processing.

Option D: Delivery delay introduces latency for new messages and does not address message re-processing.

AWS Documentation References:

Using SQS Visibility Timeout

The solution that will meet the requirements is to create an Amazon EventBridge rule that runs on a regular schedule to invoke the Lambda function. This way, the developer can use an automated and serverless way to invoke the function every 10 minutes. The developer can also use a cron expression or a rate expression to specify the schedule for the rule. The other options either involve using an Amazon EC2 instance, which is not serverless, or using environment variables or query parameters, which do not trigger the function.

This solution allows the CloudFormation service role to access the S3 bucket from any account, as long as it has the S3 GetObject permission. The bucket policy grants access to any principal with the GetObject permission, which is the least privilege needed to deploy the Lambda code. This is more secure than granting ListBucket permission, which is not required for deploying Lambda code, or using a service-based link, which is not supported for Lambda functions.

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.

References:

Amazon Cognito Documentation https://docs.aws.amazon.com/cognito/

Cognito User Pools for Web Applications: https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-pools-app-integration.html

Comprehensive and Detailed Step-by-Step Explanation:

Option A: Amazon Cognito Identity Pool with Unauthenticated Role

Cognito identity pools can generate temporary AWS credentials for both authenticated and unauthenticated users.

For guest users, Cognito assigns an unauthenticated role with limited permissions, ensuring secure access to only their resources.

This is the most secure and efficient solution for managing AWS credentials dynamically without hardcoding or storing them.

Why Other Options Are Incorrect:

Option B: Hardcoding IAM credentials in the application is insecure and violates best practices.

Option C and D: Temporary keys stored in KMS or Secrets Manager require additional implementation overhead and do not inherently manage user-specific access.

References:

Amazon Cognito Identity Pools

To meet the requirement:

Users must be able to upload (PutObject) and read (GetObject) files but not delete them.

Option D ensures users cannot delete files by omitting the s3:DeleteObject action while allowing s3:GetObject and s3:PutObject.

Option A: Includes s3:DeleteObject, which allows users to delete files and does not meet the requirement.

Option B: Contains unrelated actions like CreateBucket, which is not relevant here.

Option C: Adds s3:PutObjectRetention, which is unnecessary and does not restrict DeleteObject.

Problem: Directory access without file names fails.

S3 Static Website Hosting:

Configuring S3 as a static website enables automatic serving of index.html for directory requests.

Bucket policies ensure correct access permissions.

Updating the CloudFront origin simplifies routing.

Avoiding Public Exposure: The S3 website endpoint allows CloudFront to access content without making the bucket public.

References:

S3 Static Website Hosting: https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html

To monitor memory usage in Amazon Elastic Beanstalk environments, it's important to understand that default Elastic Beanstalk monitoring capabilities in Amazon CloudWatch do not track memory usage, as memory metrics are not collected by default. Instead, the Amazon CloudWatch agent must be configured to collect memory usage metrics.

Why Option C is Correct:

The Amazon CloudWatch agent can be installed and configured to monitor system-level metrics such as memory and disk utilization.

To enable memory tracking, developers need to install the CloudWatch agent on the Amazon Elastic Compute Cloud (EC2) instances associated with the Elastic Beanstalk environment.

After installation, the agent can be configured to collect memory metrics, which can then be sent to CloudWatch for further analysis.

How to Implement This Solution:

Install the CloudWatch Agent:Use .ebextensions or AWS Systems Manager to install and configure the CloudWatch agent on the EC2 instances running in the Elastic Beanstalk environment.

Modify CloudWatch Agent Configuration:Create a config.json file that specifies memory usage tracking and other desired metrics.

Enable Metrics Reporting:The CloudWatch agent can push the metrics to CloudWatch, where they can be monitored.

Why Other Options are Incorrect:

Option A: Configuring the agent to push logs is not sufficient to track memory metrics. This option addresses logging but not system-level metrics like memory usage.

Option B: The .ebextensions directory is used to customize Elastic Beanstalk environments but does not directly track memory metrics without additional configuration of the CloudWatch agent.

Option D: Configuring a CloudWatch dashboard will only visualize the metrics that are already being collected. It will not enable memory usage tracking.

AWS Documentation References:

Amazon CloudWatch Agent Overview

Elastic Beanstalk Customization Using .ebextensions

Monitoring Custom Metrics

Why Option B is Correct:RDS Proxy manages database connections efficiently, reducing overhead on the RDS instance and mitigating "too many connections" errors.

Why Other Options are Incorrect:

Option A: DAX is for DynamoDB, not RDS.

Option C: Migration to PostgreSQL does not address the current issue.

Option D: ElastiCache is useful for caching but does not solve connection pool issues.

AWS Documentation References:

Amazon RDS Proxy

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.

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 solution that will meet the requirements is to write data to Amazon ElastiCache. This way, the application can write session data to a fast, scalable, and reliable in-memory data store that can be served reliably across multiple requests. The other options either involve writing data to persistent storage, which is slower and more expensive than in-memory storage, or writing data to the root filesystem, which is not shared among multiple EC2 instances.

The correct answer is A. Add a configuration file in TOML format to group configuration entries to every environment. Add a table for each testing and staging environment. Deploy updates to the environments by using the sam deploy command and the --config-env flag that corresponds to the each environment.

A. Add a configuration file in TOML format to group configuration entries to every environment. Add a table for each testing and staging environment. Deploy updates to the environments by using the sam deploy command and the --config-env flag that corresponds to the each environment. This is correct. This solution will meet the requirements with the least development effort, because it uses a feature of the AWS SAM CLI that supports a project-level configuration file that can be used to configure AWS SAM CLI command parameter values1. The configuration file can have multiple environments, each with its own set of parameter values, such as stack name, region, capabilities, and more2. The developer can use the --config-env option to specify which environment to use when deploying the application3. This way, the developer can avoid creating multiple templates or scripts, or manually overriding parameters for each environment.

B. Create additional AWS SAM templates for each testing and staging environment. Write a custom shell script that uses the sam deploy command and the --template-file flag to deploy updates to the environments. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires creating and maintaining multiple templates and scripts for each environment. This can introduce duplication, inconsistency, and complexity in the deployment process.

C. Create one AWS SAM configuration file that has default parameters. Perform updates to the testing and staging environments by using the —parameter-overrides flag in the AWS SAM CLI and the parameters that the updates will override. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires manually specifying and overriding parameters for each environment every time the developer deploys the application. This can be error-prone, tedious, and inefficient.

D. Use the existing AWS SAM template. Add additional parameters to configure specific attributes for the serverless function and database table resources that are in each environment. Deploy updates to the testing and staging environments by using the sam deploy command. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires modifying the existing template and adding complexity to the resource definitions for each environment. This can also make it difficult to manage and track changes across different environments.

References:

1: AWS SAM CLI configuration file - AWS Serverless Application Model

2: Configuration file basics - AWS Serverless Application Model

3: Specify a configuration file - AWS Serverless Application Model

https://aws.amazon.com/premiumsupport/knowledge-center/secrets-manager-share-between-accounts/ https://docs.aws.amazon.com/secretsmanager/latest/userguide/auth-and-access_examples_cross.html

The solution that will meet the requirements is to use CloudFormation to create an AWS Secrets Manager secret. Use a CloudFormation dynamic reference to retrieve the secret’s value for the OpenSearch Service domain’s MasterUserOptions. Create an IAM role that has the secretsmanager:GetSecretValue permission. Assign the role to the Lambda function. Store the secret’s name as the Lambda function’s environment variable. Resolve the secret’s value at runtime. This way, the developer can pass the credentials to the Lambda function in a secure way, as AWS Secrets Manager encrypts and manages the secrets. The developer can also use a dynamic reference to avoid exposing the secret’s value in plain text in the CloudFormation template. The other options either involve passing the credentials as plain text parameters, which is not secure, or encrypting them with AWS KMS, which is less convenient than using AWS Secrets Manager.

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

The correct answer is C. The developer did not update the Docker image tag to a new version.

C. The developer did not update the Docker image tag to a new version. This is correct. When deploying an application to Amazon EKS, the developer needs to specify the Docker image tag that contains the application code and configuration. If the developer does not update the Docker image tag to a new version after making changes to the application, the EKS cluster will continue to use the old Docker image tag that references the original backend resources. To fix this issue, the developer should update the Docker image tag to a new version and redeploy the application to the EKS cluster.

A. The developer did not successfully create the new AWS account. This is incorrect. The creation of a new AWS account is not related to the application’s connection to the backend resources. The developer can use any AWS account to host the EKS cluster and the backend resources, as long as they have the proper permissions and configurations.

B. The developer added a new tag to the Docker image. This is incorrect. Adding a new tag to the Docker image is not enough to deploy the changes to the application. The developer also needs to update the Docker image tag in the EKS cluster configuration, so that the EKS cluster can pull and run the new Docker image.

D. The developer pushed the changes to a new Docker image tag. This is incorrect. Pushing the changes to a new Docker image tag is not enough to deploy the changes to the application. The developer also needs to update the Docker image tag in the EKS cluster configuration, so that the EKS cluster can pull and run the new Docker image.

References:

1: Amazon EKS User Guide, “Deploying applications to your Amazon EKS cluster”, https://docs.aws.amazon.com/eks/latest/userguide/deploying-applications.html

2: Amazon ECR User Guide, “Pushing an image”, https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-push-ecr-image.html

3: Amazon EKS User Guide, “Updating an Amazon EKS cluster”, https://docs.aws.amazon.com/eks/latest/userguide/update-cluster.html

CloudFormation and Dynamic References: The DefinitionSubstitutions property in CloudFormation allows you to pass values into Step Functions state machines at runtime.

Cost-Effectiveness: This solution is cost-effective as it leverages CloudFormation's built-in capabilities, avoiding the need for additional services like Secrets Manager or AppConfig.

References:

AWS Step Functions State Machine: https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-stepfunctions-statemachine.html

CloudFormation DefinitionSubstitutions: https://github.com/aws-cloudformation/aws-cloudformation-resource-providers-stepfunctions/issues/14

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and consistent performance with seamless scalability. The developer can create a DynamoDB table and configure the table with a primary key that consists of the title as the partition key and the release year as the sort key. This will enable querying for a given title and release year efficiently. The developer can also create a global secondary index that uses the genre as the partition key and the title as the sort key. This will enable querying for a given genre efficiently. The developer can store additional properties about the cast and production crew as attributes in the DynamoDB table. These attributes can have different data types and structures, and they do not need to be consistent across items.

References:

[Amazon DynamoDB]

[Working with Queries - Amazon DynamoDB]

[Working with Global Secondary Indexes - Amazon DynamoDB]

Step Functions Retriers: Retriers provide a built-in way to gracefully handle transient errors within State Machines. Here's how to use them:

Directly attach a retrier to the problematic 'GetResource' task.

Configure the retrier:

ErrorEquals: Set this to ['TooManyRequestsException'] to target the specific error.

IntervalSeconds: Set to 10 for the desired retry delay.

MaxAttempts: Set to 1, as you want only one retry attempt.

Error Handling:

Upon 'TooManyRequestsException', the retrier triggers the task again after 10 seconds.

On a second failure, Step Functions moves to the next state or fails the workflow, as per your design.

'IllegalArgumentException' causes error propagation as intended.

References:

Error Handling in Step Functions: https://docs.aws.amazon.com/step-functions/latest/dg/concepts-error-handling.html

AWS Lambda is a service that lets developers run code without provisioning or managing servers. Lambda layers are a distribution mechanism for libraries, custom runtimes, and other dependencies. The developer can create a Lambda layer with the required Python library and use the layer in both Lambda functions. This will reduce the size of the Lambda deployment packages and avoid reaching the quota for the maximum size of zipped deployment packages. The developer can also benefit from using layers to manage dependencies separately from function code.

References:

[What Is AWS Lambda? - AWS Lambda]

[AWS Lambda Layers - AWS Lambda]

The HTTP header that the developer should use for this analysis is the X-Forwarded-For header. This header contains the IP address of the client that made the request to the Application Load Balancer. The developer can use this header to analyze patterns for the client IP addresses that use the application. The other headers either contain information about the protocol, host, or port of the request, which are not relevant for the analysis.

AWS Secrets Manager is a service that allows you to store and manage secrets, such as database credentials, API keys, and passwords, in a secure and centralized way. It also provides features such as automatic secret rotation, auditing, and monitoring1. By using AWS Secrets Manager, you can avoid hardcoding credentials in your code, which is a bad security practice and makes it difficult to update them. You can also replicate your secrets to another Region, which is useful for disaster recovery purposes2. To access your secrets from your application, you can use the ARN of the secret, which is a unique identifier that includes the Region name. This way, your application can use the appropriate secret based on the Region where it is deployed3.

References:

AWS Secrets Manager

Replicating and sharing secrets

Using your own encryption keys

This solution will limit the ability to download a premier content file in the S3 bucket to paid subscribers only because it uses a pre-signed object URL that grants temporary access to an S3 object for a specified duration. The pre-signed object URL can be generated by the company’s website when a paid subscriber requests a download, and can be verified by Amazon S3 using the signature in the URL. Option A is not optimal because it will allow anyone to download the content from the S3 bucket without verifying their subscription status. Option C is not optimal because it will require additional steps and costs to configure multi-factor authentication for accessing the S3 bucket objects, which may not be feasible or user-friendly for paid subscribers. Option D is not optimal because it will not prevent non-paying website visitors from accessing the S3 bucket objects, but only encrypt them at rest.

References: Share an Object with Others, [Using Amazon S3 Pre-Signed URLs]

Comprehensive and Detailed Step-by-Step Explanation:

Customer Managed Keys (CMK) for Granular Control (Option B):

Customer-managed KMS keys are required to meet the security policy requirement of team-specific control over KMS key rotation. Each team can manage the lifecycle of its own key.

The kms:Decrypt permission allows the Lambda function execution roles to decrypt the environment variables during runtime.

This solution adheres to the principle of least privilege and satisfies the need for team-specific key control.

Why Other Options Are Incorrect:

Option A: AWS-managed keys cannot provide team-specific control or support the custom rotation policy required by the teams.

Option C: Adding kms:CreateGrant and kms:Encrypt permissions to Lambda roles is unnecessary for this scenario. The key usage is limited to decryption at runtime.

Option D: AWS-managed keys still lack team-specific control, and adding kms:CreateGrant and kms:Encrypt is redundant.

References:

AWS Lambda Environment Variables

AWS Key Management Service Documentation

Comprehensive and Detailed Step-by-Step Explanation:To confirm that the HTTP headers, body, and responses meet expectations, you need to test the specific HTTP Task state in isolation and inspect the details.

Option A: TestState API with TRACE:

The TestState API allows developers to test individual states in a state machine without executing the entire workflow.

Setting the inspection level to TRACE provides detailed information about the HTTP request and response, including headers, body, and status codes.

This option provides the precise and granular testing required to verify the HTTP Task functionality.

Why Other Options Are Incorrect:

Option B: The DEBUG inspection level provides less detailed information than TRACE and focuses on general debugging, not a detailed view of HTTP interactions.

Option C: Step Functions does not have a "data flow simulator" to test individual tasks; this option is not valid.

Option D: Changing the state machine’s log level to ALL increases logging granularity for the entire state machine but does not allow isolated testing of a specific HTTP Task.

References:

AWS Step Functions: Testing State Machines

API Gateway Stage Variables: These are designed for configuring dynamic values for your APIs in different deployment stages (dev, test, prod). Here's how to use them for third-party endpoints:

In the API Gateway console, access the "Stages" section of your API.

For each stage, create a stage variable named something like thirdPartyEndpoint.

Set the value of this variable to the actual endpoint URL for that specific environment.

When configuring API requests within your API Gateway method, reference this endpoint using ${stageVariables.thirdPartyEndpoint}.

Why Stage Variables Excel Here:

Environment Isolation: This approach keeps the endpoint configuration specific to each deployment stage, ensuring the right endpoints are used during development, testing, and production cycles.

Ease of Management: You manage the endpoints directly through the API Gateway console without additional infrastructure.

References:

Amazon API Gateway Stage Variables: https://docs.aws.amazon.com/apigateway/latest/developerguide/stage-variables.html

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 solution will meet the requirements with the least operational overhead because it uses Amazon S3 as a scalable, secure, and durable storage service for the reports. The presigned URL will allow customers to access their reports for a limited time (8 hours) without requiring additional authentication. The S3 Lifecycle configuration rules will automatically delete the reports that are older than 2 days, reducing storage costs and complying with the data retention policy. Option A is not optimal because it will incur additional costs and complexity to store the reports as DynamoDB items, which have a size limit of 400 KB. Option B is not optimal because it will not provide customers with access to their reports within one hour, as Amazon SNS email delivery is not guaranteed. Option D is not optimal because it will require more operational overhead to manage an RDS database and a Lambda function for storing and deleting the reports.

References: Amazon S3 Presigned URLs, Amazon S3 Lifecycle

This solution allows the Lambda function to be invoked by the DynamoDB stream whenever updates are made to items in the DynamoDB table. Event source mapping is a feature of Lambda that enables a function to be triggered by an event source, such as a DynamoDB stream, an Amazon Kinesis stream, or an Amazon Simple Queue Service (SQS) queue. The developer can configure event source mapping for the Lambda function using the AWS Management Console, the AWS CLI, or the AWS SDKs. Changing the StreamViewType parameter value to NEW_AND_OLD_IMAGES for the DynamoDB table will not affect the invocation of the Lambda function, but only change the information that is written to the stream record. Mapping an Amazon Simple Notification Service (Amazon SNS) topic to the DynamoDB stream will not invoke the Lambda function directly, but require an additional subscription from the Lambda function to the SNS topic. Increasing the maximum runtime (timeout) setting of the Lambda function will not affect the invocation of the Lambda function, but only change how long the function can run before it is terminated.

The solution that will meet the requirements is to use an AWS Step Functions state machine to monitor API failures. Use the Wait state to delay calling the Lambda function. This way, the developer can refactor the serverless application to accommodate the change in a way that is automated and scalable. The developer can use Step Functions to orchestrate the Lambda function and handle any errors or retries. The developer can also use the Wait state to pause the execution for a specified duration or until a specified timestamp, which can help avoid exceeding the API limits. The other options either involve using additional services that are not necessary or appropriate for this scenario, or do not address the issue of API failures.

This solution meets the requirements with the least amount of configuration because it uses a feature of AWS CDK that allows custom logic to be executed during stack deployment or deletion. The AWS Cloud Development Kit (AWS CDK) is a software development framework that allows you to define cloud infrastructure as code and provision it through CloudFormation. An AWS CDK custom resource is a construct that enables you to create resources that are not natively supported by CloudFormation or perform tasks that are not supported by CloudFormation during stack deployment or deletion. The developer can write a handler function in the code that uses AWS SDK calls to check for and delete unused resources, and create an AWS CDK custom resource that attaches the function code to a Lambda function and invokes it when the deployment stack runs. This way, the developer can automate the cleanup process without requiring additional configuration or integration. Creating a CloudFormation template from a JSON file will require additional configuration and integration with the central AWS CDK application. Creating an API in AWS Amplify will require additional configuration and integration with the central AWS CDK application and may not provide optimal performance or availability. Writing a handler function in the AWS Lambda console will require additional configuration and integration with the central AWS CDK application.

Visibility Timeout: When an SQS message is processed by a consumer (here, the Lambda function), it's temporarily hidden from other consumers. Visibility timeout controls this duration.

How It Helps:

Increase the visibility timeout beyond the maximum processing time your Lambda might typically take for long videos.

This prevents the message from reappearing in the queue while Lambda is still working, avoiding premature timeouts.

References:

SQS Visibility Timeout: https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-visibility-timeout.html

DynamoDB distributes throughput across partitions based on the hash key. A hot partition (caused by high usage of a specific hash key) can result in a ProvisionedThroughputExceededException, even if overall usage is below the provisioned capacity.

Why Option B is Correct:

Partition-Level Limits: Each partition has a limit of 3,000 read capacity units or 1,000 write capacity units per second.

Hot Partition: Excessive use of a single hash key can overwhelm its partition.

Why Not Other Options:

Option A: DynamoDB storage size does not affect throughput.

Option C: Provisioned scaling operations are unrelated to throughput errors.

Option D: Sort keys do not impact partition-level throughput.

References:

DynamoDB Partition Key Design Best Practices

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.

The AWS Serverless Application Model (AWS SAM) comes built-in with CodeDeploy to provide gradual AWS Lambda deployments1. The DeploymentPreference property in AWS SAM allows you to specify the type of deployment that you want. The Canary10Percent10Minutes option means that 10 percent of your customer traffic is immediately shifted to your new version. After 10 minutes, all traffic is shifted to the new version1. The AutoPublishAlias property in AWS SAM allows AWS SAM to automatically create an alias that points to the updated version of the Lambda function1. Therefore, option A is correct.

AWS CloudFormation is a service that allows developers to model and provision AWS resources using templates. A CloudFormation template can define the load test resources, such as EC2 instances, load balancers, and Auto Scaling groups. A CloudFormation stack set is a collection of stacks that can be created and managed from a single template in multiple Regions and accounts. The AWS CLI create-stack-set command can be used to create a stack set from a template and specify the Regions where the stacks should be created. Reference: Working with AWS CloudFormation stack sets

Why Option A is Correct:Running an AWS Lambda function within the same VPC ensures secure communication without exposing the API application to public IP addresses. The Lambda function can serve as a secure EventBridge target to send events to the API.

Why Other Options are Incorrect:

Option B & C: Internet-facing load balancers expose public IP addresses, which violates compliance requirements.

Option D: EventBridge cannot directly target an endpoint within a private VPC without intermediary services like Lambda.

AWS Documentation References:

EventBridge Targets

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.

References: AWS Systems Manager Parameter Store, [Using SSL/TLS to Encrypt a Connection to a DB Instance]

Amazon ElastiCache for Memcached is a fully managed, multithreaded, and scalable in-memory key-value store that can be used to cache frequently accessed data and improve application performance1. By using Amazon ElastiCache for Memcached, the developer can reduce the load on the main database and handle high traffic surges more efficiently.

To use Amazon ElastiCache for Memcached, the developer needs to create a cache cluster with one or more nodes, and configure the application to store and retrieve data from the cache cluster2. The developer can use any of the supported Memcached clients to interact with the cache cluster3. The developer can also use Auto Discovery to dynamically discover and connect to all cache nodes in a cluster4.

Amazon ElastiCache for Memcached is compatible with the Memcached protocol, which means that the developer can use existing tools and libraries that work with Memcached1. Amazon ElastiCache for Memcached also supports data partitioning, which allows the developer to distribute data among multiple nodes and scale out the cache cluster as needed.

Using Amazon ElastiCache for Memcached is a simple and effective solution that meets the requirements with the least complexity. The developer does not need to change the database schema, migrate data to a different service, or use a different caching model. The developer can leverage the existing Memcached ecosystem and easily integrate it with the application.

An SQS FIFO queue is a type of queue that preserves the order of messages and ensures that each message is delivered and processed only once1. This is suitable for the scenario where the developer wants to ensure that there are no out-of-order updates in the legacy system.

The visibility timeout value is the amount of time that a message is invisible in the queue after a consumer receives it2. This prevents other consumers from processing the same message simultaneously. If the consumer does not delete the message before the visibility timeout expires, the message becomes visible again and another consumer can receive it2.

In this scenario, the developer needs to configure the visibility timeout value to be longer than the maximum processing time of the legacy system, which is 5 minutes. This will ensure that the message remains invisible in the queue until the legacy system finishes processing it and deletes it. This will prevent duplicate or out-of-order processing of messages by the legacy system.

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.

References:

[What Is the AWS Serverless Application Model (AWS SAM)? - AWS Serverless Application Model]

[AWS SAM Template Specification - AWS Serverless Application Model]

Lambda Layers: These are designed to package dependencies that you can share across functions.

How to Use:

Create a layer, upload your 100MB library as a zip.

Attach the layer to your function.

In your function code, import the library from the standard layer path.

References:

Lambda Layers: https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html

The correct policy condition ensures that:

The LeadingKeys condition restricts operations to the authenticated user's user_id.

The Attributes condition limits the updatable attributes to user_name.

Explanation of Choices:

Option A: Correctly enforces both the key restriction (dynamodb:LeadingKeys) and ensures only the user_name attribute can be updated.

Option B, C, D: Use incorrect conditions, such as referencing user_name in the LeadingKeys or including other attributes like user_id in updatable fields.

Principle of Least Privilege: Granting specific permissions through an IAM role is more secure than directly attaching policies to a function or using root user credentials.

IAM Roles for Lambda: Designed to provide temporary credentials to Lambda functions, enhancing security.

Reusability: The existing IAM policy ensures the correct S3 and DynamoDB access is granted.

References:

IAM Roles for Lambda Documentation: https://docs.aws.amazon.com/lambda/latest/dg/lambda-intro-execution-role.html

IAM Best Practices: https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html

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.

References:

AWS::Lambda::Function Resource https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-lambda-function.html