Amazon Web Services DOP-C02 Exam With Confidence Using Practice Dumps

When you create a pipeline from CodePipeline during the step-by-step it creates a CloudWatch Event rule for a given branch and repo

like this:

{

" source " : [

" aws.codecommit "

],

" detail-type " : [

" CodeCommit Repository State Change "

],

" resources " : [

" arn:aws:codecommit:us-east-1:xxxxx:repo-name "

],

" detail " : {

" event " : [

" referenceCreated " ,

" referenceUpdated "

],

" referenceType " : [

" branch "

],

" referenceName " : [

" master "

]

}

}

These solutions will meet the requirement because they will prevent the loss of notification messages in the future. An Amazon SQS queue is a service that provides a reliable, scalable, and secure message queue for asynchronous communication between distributed components. You can use an SQS queue to buffer messages from an SNS topic and ensure that they are delivered and processed by a Lambda function, even if the function or the database is temporarily unavailable.

Option C will configure an SQS dead-letter queue for the SNS topic. A dead-letter queue is a queue that receives messages that could not be delivered to any subscriber after a specified number of retries. You can use a dead-letter queue to store and analyze failed messages, or to reprocess them later. This way, you can avoid losing messages that could not be delivered to the Lambda function due to network errors, throttling, or other issues.

Option D will subscribe an SQS queue to the SNS topic and configure the Lambda function to process messages from the SQS queue. This will decouple the SNS topic from the Lambda function and provide more flexibility and control over the message delivery and processing. You can use an SQS queue to store messages from the SNS topic until they are ready to be processed by the Lambda function, and also to retry processing in case of failures. This way, you can avoid losing messages that could not be processed by the Lambda function due to database errors, timeouts, or other issues.

The requirement is to automatically apply different baseline CloudFormation templates based on OU placement when new AWS accounts are created, while keeping operational overhead as low as possible. Because the company is already using AWS Organizations and is planning a comprehensive account management strategy, the most AWS-native and efficient solution is AWS Control Tower with Customizations for AWS Control Tower (CfCT) .

CfCT is specifically designed to extend Control Tower’s baseline by allowing administrators to deploy OU-scoped CloudFormation templates automatically. The solution uses a manifest file to map CloudFormation templates to specific OUs, ensuring that each new account receives the correct baseline configuration immediately after provisioning. Templates and configuration are stored in a version-controlled Git repository, providing auditability, change tracking, and rollback capabilities.

Option B adds unnecessary operational complexity by introducing a custom CodePipeline that must be manually triggered and maintained. This duplicates functionality that CfCT already provides natively. Options C and D rely on custom Lambda logic and EventBridge rules, which increase maintenance burden, reduce transparency, and lack built-in OU-aware governance features.

AWS documentation explicitly recommends Customizations for AWS Control Tower for OU-based, scalable, and automated baseline deployments. Therefore, Option A delivers the required functionality with the least operational overhead and aligns with AWS best practices for multi-account governance.