Amazon Web Services Updated DVA-C02 Exam Questions and Answers by mahnoor

Requirement Summary:

Users experience buffering/delay when starting video stream

Architecture:

CloudFront → API Gateway → Lambda → DynamoDB

Need to identify root cause of performance issues

Evaluate Options:

A: Enable AWS X-Ray tracing

Ideal for end-to-end tracing

Visualizes latency across services (API Gateway, Lambda, DynamoDB)

Creates a service map for easy identification of bottlenecks or errors

Designed specifically for distributed tracing and performance monitoring

B: CloudWatch Logs Insights

⚠️ Helpful for querying logs

But lacks the visual trace linkage across services like X-Ray

Does not identify where latency accumulates

C: AWS Config

Tracks configuration changes, not runtime performance

D: CloudTrail + CloudWatch Logs

More useful for audit/logging, not tracing performance or latency issues

X-Ray overview: https://docs.aws.amazon.com/xray/latest/devguide/aws-xray.html

Service map: https://docs.aws.amazon.com/xray/latest/devguide/xray-console-service-map.html

Tracing API Gateway: https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-xray.html

This solution will meet the requirements by using Amazon CloudWatch Logs to capture and analyze the logs from API Gateway. Amazon CloudWatch Logs is a service that monitors, stores, and accesses log files from AWS resources. The developer can turn on execution logging and access logging in Amazon CloudWatch Logs for the API stage, which enables logging information about API execution and client access to the API. The developer can create a CloudWatch Logs log group, which is a collection of log streams that share the same retention, monitoring, and access control settings. The developer can specify the Amazon Resource Name (ARN) of the log group for the API stage, which instructs API Gateway to send the logs to the specified log group. The developer can then examine the logs to determine the cause of the HTTP 400 response errors. Option A is not optimal because it will create an Amazon Kinesis Data Firehose delivery stream to receive API call logs from API Gateway, which may introduce additional costs and complexity for delivering and processing streaming data. Option B is not optimal because it will turn on AWS CloudTrail Insights and create a trail, which is a feature that helps identify and troubleshoot unusual API activity or operational issues, not HTTP response errors. Option C is not optimal because it will turn on AWS X-Ray for the API stage, which is a service that helps analyze and debug distributed applications, not HTTP response errors.

This solution allows the developer to troubleshoot the failure by capturing unprocessed events in a queue for further analysis. Dead Letter Queues (DLQs) are queues that store messages that could not be processed by a service, such as Lambda, for various reasons, such as configuration errors, throttling limits, or permissions issues. The developer can configure DLQs for Lambda functions by sending events to either an Amazon Simple Queue Service (SQS) queue or an Amazon Simple Notification Service (SNS) topic. The developer can then inspect the messages in the queue or topic to identify and fix the root cause of the failure. Configuring AWS CloudTrail logging will not capture invocation failures for asynchronous Lambda invocations, but only record API calls made by or on behalf of Lambda. Configuring Amazon Simple Workflow Service (SWF) or AWS Config will not process any direct unprocessed events, but require additional integration and configuration.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS-doc-aligned):

The most operationally efficient way to share and patch common dependencies across multiple Lambda functions is to use AWS Lambda layers . A Lambda layer is an independently managed deployment artifact that can contain libraries and other dependencies. Multiple functions can reference the same layer, which avoids packaging identical open source modules into each function bundle.

When a vulnerability or bug is found in a shared module, you patch it once by publishing a new layer version that contains the updated dependencies. Then you update each Lambda function configuration to reference the new layer version. This centralizes dependency management, reduces duplicated artifacts, and makes rollouts and rollbacks cleaner (you can switch layer versions quickly). It also keeps function deployment packages smaller, which often improves deployment speed and can reduce cold-start overhead associated with larger bundles.

The other options introduce unnecessary operational burden:

Downloading modules at cold start (CloudFront/S3) increases latency and adds failure modes at runtime.

Hosting a private registry on EC2 requires patching, scaling, availability management, and security hardening—high operational overhead.

A CloudFormation registry extension and daily scans are not a standard or efficient mechanism for packaging runtime Node.js dependencies into Lambda functions.

Lambda layers are a documented AWS-native feature intended for precisely this use case: sharing common code and libraries across functions while enabling controlled versioning. Therefore, creating a layer for the open source modules and updating the functions to consume the layer is the most efficient and maintainable approach.