Free Practice Questions for Amazon Web Services DVA-C02 Exam

AWS Lambda layers are specifically designed to share common code and dependencies across multiple Lambda functions. A layer is a ZIP archive that contains libraries, a custom runtime, or other function dependencies. Layers are versioned, so the developer can publish a new layer version when libraries change and then update functions to reference the new version. This directly meets the requirements to centralize libraries, update conveniently, and keep libraries versioned—while requiring minimal development work.

Using a single shared layer significantly reduces duplication across function deployment packages. It also simplifies CI/CD because the developer can build and publish the layer independently and reuse it across many functions.

Option A (CodeArtifact) is useful for dependency management, but it still requires each Lambda deployment to package dependencies or download them during build. It’s more moving parts than a layer for this use case.

Option B (container images) can centralize dependencies but typically requires more effort: building, scanning, versioning container images, and updating function image URIs. It’s heavier operationally than layers for “shared custom libraries.”

Option D (EFS) can be mounted by Lambda, but introduces networking considerations (VPC config), EFS lifecycle/permissions, and is unnecessary for simply sharing libraries. Also, cold start and operational overhead can increase.

Therefore, creating a Lambda layer for the shared libraries is the least-effort, most standard approach.

AWS X-Ray SDK: The primary way to enable X-Ray tracing within applications. The SDK sends data about requests and subsegments to the X-Ray daemon for service map generation.

Instrumenting All Services: To visualize a complete microservice architecture on the service map, each relevant service must include the X-Ray SDK.

CloudFormation and Dynamic References: The DefinitionSubstitutions property in CloudFormation allows you to pass values into Step Functions state machines at runtime.

Cost-Effectiveness: This solution is cost-effective as it leverages CloudFormation's built-in capabilities, avoiding the need for additional services like Secrets Manager or AppConfig.

Amazon Machine Images (AMIs) are encrypted snapshots of EC2 instances that can be used to launch new instances. The developer can create new AMIs from the existing instances and specify encryption parameters. The developer can copy the encrypted AMIs to the destination Region and use them to create a new application stack. The developer can delete the unencrypted AMIs after the encryption process is complete. This solution will meet the encryption requirement and allow the developer to expand the application to run in the destination Region.

The Deployment Preference Type property specifies how traffic should be shifted between versions of a Lambda function1. The Canary10Percent10Minutes option means that 10% of the traffic is immediately shifted to the new version, and after 10 minutes, the remaining 90% of the traffic is shifted1. This matches the requirement of shifting 10% of the traffic for the first 10 minutes, and then switching all traffic to the new version.

The AutoPublishAlias property enables AWS SAM to automatically create and update a Lambda alias that points to the latest version of the function1. This is required to use the Deployment Preference Type property1. The alias name can be specified by the developer, and it can be used to invoke the function with the latest code.

The core requirement is to perform a one-time deployment for testing without impacting the existing pre-production stack that other team members rely on. The safest way to avoid disruption is to deploy into an isolated AWS account (a dedicated development/testing account) instead of modifying the shared pre-production environment.

Option C achieves this with the least effort: use the AWS SAM CLI to package and deploy the same SAM application into a new AWS account designated for development. This creates an isolated copy of the stack (Lambda + SNS topics) where the developer can validate changes without affecting the shared pre-production deployment or the release pipeline.

Option A still deploys into pre-production, which directly violates the requirement to avoid impacting the existing application. A “debug parameter” does not prevent resource updates or side effects.

Option B is inconsistent: a CloudFormation change set is created against a specific account/stack. You cannot create a change set in one account and “execute it” in a different account as stated. Cross-account execution would require different credentials and a separate deployment process.

Option D introduces additional pipeline/stage complexity and is not necessary for a one-time test; it also implies changing the existing stack/pipeline, which can itself cause disruption.

This benefit occurs when initializing the AWS SDK outside of the Lambda handler function because it allows the SDK instance to be reused across multiple invocations of the same function. This can improve performance and reduce latency by avoiding unnecessary initialization overhead. If the SDK is initialized inside the handler function, it will create a new SDK instance for each invocation, which can increase memory usage and execution time.