A company wants to decrease the time it takes to develop new features. The company uses AWS CodeBuild and AWS CodeDeploy to build and deploy its applications. The company uses AWS CodePipeline to…

Question

Options

• AProgram an AWS Lambda function that creates Amazon CloudWatch custom metrics with
• BProgram an AWS Lambda function that creates Amazon CloudWatch custom metrics with
• CProgram an AWS Lambda function that writes information about successful runs and failed runs
• DProgram an AWS Lambda function that writes information about successful runs and failed runs

• A
  18% (7)
• B
  67% (26)
• C
  10% (4)
• D
  5% (2)

Explanation

Explanation

Option B is correct because AWS CodePipeline natively integrates with Amazon CloudWatch metrics, and using a Lambda function that creates CloudWatch custom metrics leveraging CodePipeline's built-in execution state change events (via Amazon EventBridge) provides the least configuration effort - specifically, it can track lead time for changes (time from commit to deployment) and mean time to recovery (MTTR) using existing pipeline data without additional tooling. The key differentiator for B over A is that B uses CodePipeline execution events as the data source, which are already available natively, requiring minimal setup. Options C and D are incorrect because writing information to an external store (such as a database or S3) adds unnecessary complexity and additional configuration steps - querying and visualizing that data requires extra services compared to CloudWatch's built-in dashboards and alarms. The distinction between C and D likely involves where the data is stored, but both require more configuration effort than leveraging CloudWatch metrics directly.

Memory Tip: When a question asks for "least configuration effort" with CI/CD visibility metrics, think CloudWatch + EventBridge + CodePipeline events - they're already connected out of the box. If the answer involves writing to a separate data store, it's almost always the more complex option.

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