DOP-C02 Exam Dumps : AWS Certified DevOps Engineer - Professional

Step 1: Understanding the Deletion FailureThe most likely reason why the CloudFormation stack failed to delete is that the S3 bucket was not empty. AWS CloudFormation cannot delete an S3 bucket that contains objects, so if the website files are still in the bucket, the deletion will fail.

Issue: The S3 bucket is not empty during deletion, preventing the stack from being deleted.

Step 2: Modifying the Custom Resource to Handle DeletionTo mitigate this issue, you can modify the Lambda function associated with the custom resource to automatically empty the S3 bucket when the stack is being deleted. By adding logic to handle the RequestType: Delete event, the function can recursively delete all objects in the bucket before allowing the stack to be deleted.

Action: Modify the Lambda function to recursively delete the objects in the S3 bucket when RequestType is set to Delete.

Why: This ensures that the S3 bucket is empty before CloudFormation tries to delete it, preventing the stack deletion failure.

Option A is incorrect because setting the deployment configuration to LambdaAllAtOnce means that the new version of the application will be deployed to all Lambda functions at once, affecting all customers. This does not meet the requirement of affecting the fewest customers possible. Moreover, configuring automatic rollbacks on the deployment group is not operationally efficient, as it requires manual intervention to fix the errors and redeploy the application.

Option B is correct because setting the deployment configuration to LambdaCanary10Percent10Minutes means that the new version of the application will be deployed to 10 percent of the Lambda functions first, and then to the remaining 90 percent after 10 minutes. This minimizes the impact of errors on customers, as only 10 percent of them will be affected by a faulty deployment. Configuring automatic rollbacks on the deployment group also meets the requirement of reverting to the most recent stable version of the application when an error is detected. Creating a CloudWatch alarm that detects HTTP Bad Gateway errors on API Gateway is a valid way to monitor the health of the application and trigger a rollback if needed.

Option C is incorrect because setting the deployment configuration to LambdaAllAtOnce means that the new version of the application will be deployed to all Lambda functions at once, affecting all customers. This does not meet the requirement of affecting the fewest customers possible. Moreover, configuring manual rollbacks on the deployment group is not operationally efficient, as it requires human intervention to stop the current deployment and start a new one. Creating an SNS topic to send notifications every time a deployment fails is not sufficient to detect errors in the application, as it does not monitor the API Gateway responses.

Option D is incorrect because configuring manual rollbacks on the deployment group is not operationally efficient, as it requires human intervention to stop the current deployment and start a new one. Creating a metric filter on a CloudWatch log group for API Gateway to monitor HTTP Bad Gateway errors is a valid way to monitor the health of the application, but invoking a new Lambda function to perform a rollback is unnecessary and complex, as CodeDeploy already provides automatic rollback functionality.

 Modify the Build Stage to Add a Test Action with a RunOrder Value of 2:

The build stage in AWS CodePipeline can have multiple actions. By adding a test action with a runOrder value of 2, the test action will execute after the initial build action completes.

 Use AWS CodeBuild as the Action Provider to Run Unit Tests:

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages.

Using CodeBuild to run unit tests ensures that the tests are executed in a controlled environment and that only the code changes that pass the unit tests proceed to the deploy stage.

Example configuration in CodePipeline:

{

" name " : " BuildStage " ,

" actions " : [

{

" name " : " Build " ,

" actionTypeId " : {

" category " : " Build " ,

" owner " : " AWS " ,

" provider " : " CodeBuild " ,

" version " : " 1 "

},

" runOrder " : 1

},

{

" name " : " Test " ,

" actionTypeId " : {

" category " : " Test " ,

" owner " : " AWS " ,

" provider " : " CodeBuild " ,

" version " : " 1 "

},

" runOrder " : 2

}

]

}

By integrating the unit tests into the build stage and ensuring they run after the build process, the pipeline guarantees that only code changes passing all unit tests are deployed.