DVA-C02 Exam Dumps - AWS Certified Developer - Associate

This solution will meet the requirements by using a rolling with additional batch deployment method, which deploys the new version of the application to a separate group of instances and then shifts traffic to those instances in batches. This way, the application maintains full capacity and avoids service interruption during deployment, as well as minimizes the cost of additional resources that support the deployment. Option A is not optimal because it will use an all at once deployment method, which deploys the new version of the application to all instances simultaneously, which may cause service interruption or downtime during deployment. Option C is not optimal because it will use a blue/green deployment method, which deploys the new version of the application to a separate environment and then swaps URLs with the original environment, which may incur more costs for additional resources that support the deployment. Option D is not optimal because it will use an immutable deployment method, which deploys the new version of the application to a fresh group of instances and then redirects traffic to those instances, which may also incur more costs for additional resources that support the deployment.

Why Option B is Correct:The pilot light strategy keeps a minimal version of the environment in another Region and scales up during a disaster. It achieves an RTO and RPO of hours at a low cost and complexity.

Why Other Options are Incorrect:

Option A: Warm standby is more expensive as it keeps a scaled-down, fully functioning version running in another Region.

Option C: Backup and restore has a longer RTO and RPO than hours.

Option D: Multi-site active-active is costly and more complex than required.

AWS Documentation References:

Disaster Recovery Strategies on AWS

Amazon S3 is a service that provides highly scalable, durable, and secure object storage. The developer can create an S3 bucket to host the repository and migrate the existing .xml files to the S3 bucket. The developer can update the application code to use the AWS SDK to read and write configuration files from S3. This solution will meet the requirement of high availability for the repository in a cost-effective way.

DynamoDB TTL (Time-to-Live): A native feature that automatically deletes items after a specified expiration time.

Efficiency: Eliminates the need for scheduled deletion jobs, optimizing write throughput by avoiding potential throttling conflicts.

Seamless Integration: TTL works directly within DynamoDB, requiring minimal development overhead.

For a serverless web application that needs user registration and authentication with minimal configuration, the standard AWS solution is Amazon Cognito User Pools. User Pools provide managed user directories, sign-up/sign-in flows, MFA options, password reset, and token-based authentication (OAuth2/OIDC/JWT). This integrates naturally with API Gateway (Cognito authorizers) and Lambda.

Option A is best because it uses Cognito User Pools plus an app client and the Hosted UI, which provides ready-made sign-up/sign-in pages. Hosted UI minimizes custom UI and backend authentication code while still allowing branding customization and secure token issuance. The SPA can authenticate via Cognito, receive JWT tokens, and call API Gateway endpoints securely.

Option B (identity pool) is primarily for granting temporary AWS credentials to access AWS services directly (federation). It does not replace User Pools for user registration/login flows; typically identity pools are used in addition to user pools, not instead, and require more configuration.

Option C adds unnecessary infrastructure (EC2) and defeats the “minimize configuration” goal.

Option D is not appropriate for end-user authentication. IAM users/groups are for workforce/admin access, not for customer-facing apps.

Therefore, Cognito User Pool + Hosted UI is the minimal-configuration secure solution.

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.

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.