A company used AWS to develop an application for customers. The application includes an Amazon API Gateway API that invokes AWS Lambda functions. The Lambda functions process data and store the data i

Question

Options

• AInstrument the application with AWS X-Ray. Inspect the service map to identify errors and issues.
• BConfigure Lambda exceptions and additional logging to Amazon CloudWatch. Use CloudWatch
• CConfigure API Gateway to log responses to Amazon CloudWatch. Create a metric filter for the
• DUse Amazon CloudWatch metrics for the DynamoDB tables to identify all the

• A
  82% (51)
• B
  5% (3)
• C
  11% (7)
• D
  2% (1)

Explanation

AWS X-Ray is the right choice because it provides end-to-end distributed tracing across API Gateway, Lambda, and DynamoDB with minimal configuration - just enabling X-Ray tracing on each service generates a visual service map that pinpoints latency, errors, and bottlenecks automatically.

Why the distractors fall short:

• B requires manually adding exception handling and custom logging code to each Lambda function, which is significant development effort with no cross-service visibility.
• C only covers the API Gateway layer - it misses Lambda and DynamoDB, so you'd never see the full picture of where bottlenecks originate.
• D focuses solely on DynamoDB metrics, leaving API Gateway and Lambda blind spots, and requires manual metric configuration per table.

Memory tip: Think of X-Ray as the "one switch, full map" solution - whenever a question asks for least effort + end-to-end visibility across a serverless stack (API Gateway → Lambda → DynamoDB), X-Ray's service map is almost always the answer. The other options require you to stitch together monitoring piece by piece.

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