DVA-C02 Exam Dumps - AWS Certified Developer - Associate

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

Why Option B is Correct:The RetainExceptOnCreate deletion policy ensures that the IAM user is retained after successful stack creation but is deleted if the stack creation fails or rolls back. This meets both requirements.

Why Other Options are Incorrect:

Option A: The Retain policy retains the resource regardless of stack status and does not delete the IAM user upon rollback.

Option C: Updating the service role for termination protection does not address the specific deletion behavior for the IAM user.

Option D: Stack policy controls updates, not resource deletion behavior during rollbacks.

AWS Documentation References:

CloudFormation DeletionPolicy Attribute

https://docs.aws.amazon.com/lambda/latest/dg/nodejs-context.html https://docs.aws.amazon.com/lambda/latest/dg/nodejs-logging.html

There is no explicit information for the runtime, the code is written in Node.js.

AWS Lambda is a service that lets developers run code without provisioning or managing servers. The developer can use the AWS request ID field in the context object to obtain a unique identifier for each function invocation. The developer can configure the application to write logs to standard output, which will be captured by Amazon CloudWatch Logs. This solution will meet the requirement of logging key events with a unique identifier.

To create a central logging solution for microservices, using Amazon CloudWatch Logs is a recommended and effective approach. Here's why:

Amazon CloudWatch Logs Streams allow you to centralize logs from different services, which is crucial as the number of microservices increases.

Each microservice can have its own dedicated log stream within Amazon CloudWatch Logs, providing clear segregation of logs while still allowing centralized management.

This setup enables developers to monitor, search, and analyze logs efficiently using tools like CloudWatch Insights.

Other options like CloudTrail (B) are designed for API activity monitoring, not application logs. VPC Flow Logs (C) focus on network traffic rather than application behavior. AWS Cloud Map (D) is for service discovery and routing, not logging.

Using Amazon ElastiCache for Memcached to store and manage the session data will reduce the memory load and improve the performance of the web server. Using Amazon RDS for MySQL DB instance to store the application data will provide a scalable, reliable, and managed database service. Option A is not optimal because it does not address the memory issue of the web server. Option C is not optimal because it does not provide a persistent storage for the application data. Option D is not optimal because it does not provide a high availability and durability for the session data.

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.