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

The correct answer is C because for an in-place deployment on Amazon EC2 instances, AWS CodeDeploy automatic rollback works by redeploying the last known good revision when a deployment fails or when a configured monitoring alarm is triggered. AWS documentation explains that when automatic rollback is enabled, CodeDeploy creates a new deployment that uses the most recent successfully deployed application revision. This rollback deployment receives its own new deployment ID , because it is treated as a separate deployment event rather than a simple undo operation.

Option A is incorrect because CodeDeploy does not restore an Amazon S3 snapshot of the previous deployment state for EC2 in-place deployments. It redeploys the last successful revision instead. Option B describes behavior associated with blue/green deployment patterns , not in-place deployments . Route 53 alias switching and blue/green environment replacement are not how CodeDeploy handles failed validation during an EC2 in-place deployment. Option D is incorrect because CodePipeline orchestrates stages in a delivery pipeline, but it is not the service that performs the rollback behavior described here.

This distinction is important in AWS deployment design. In-place deployments update the same EC2 instances already serving the application. If validation fails, CodeDeploy does not roll traffic back through DNS or infrastructure replacement. Instead, it launches a rollback deployment of the previously successful application revision across those same instances. That behavior directly aligns with AWS CodeDeploy automatic rollback functionality for EC2/On-Premises in-place deployments.

Therefore, the correct result is that CodeDeploy redeploys the last known stable version as a new deployment , which makes C the right answer.

In AWS X-Ray, filter expressions can filter trace data based on indexed fields that X-Ray can query efficiently. Custom data can be added to segments in two main ways: annotations and metadata. The critical difference is that annotations are indexed and can be used for filtering, while metadata is not indexed and is intended for additional diagnostic context that you do not typically query on.

Therefore, if the developer wants user-specified custom attributes to be usable in X-Ray filter expressions, the developer should record those attributes as annotations in the segment document. Once recorded as annotations, the developer can write filter expressions that match annotation keys/values and return only the relevant traces.

Option B is incorrect because metadata is not indexed and cannot be used in filter expressions for trace search the same way annotations can.

Option C is incorrect because segment documents have a defined schema; adding arbitrary “new segment fields” is not the intended method for searchable custom attributes.

Option D is unrelated: sampling rules control which requests get traced in the first place. They are not used to filter returned results by custom attributes after traces are recorded.

Comprehensive and Detailed Step-by-Step Explanation:

Option D: Use Lambda with Container Images

AWS Lambda supports container images up to 10 GB in size, making it suitable for applications with large dependencies, such as a video codec library larger than 250 MB.

By creating separate container images for video compression and decompression, the application can efficiently isolate functionality while ensuring that each function includes the required dependencies.

The container images are stored in Amazon ECR and used to create the Lambda functions.

Why Other Options Are Incorrect:

Option A: A single Lambda function with all functionalities and dependencies in one zip file is not feasible due to the 250 MB deployment package size limit for zip files.

Option B: Including the library in two separate zip files still exceeds the size limit for Lambda zip deployment packages.

Option C: While using a Lambda layer can reduce redundancy, the combined size of the layer and the zip files would exceed the limit of 250 MB.