Pass the Amazon Web Services AWS Certified Associate DVA-C02 Questions and answers with CertsForce

The solution that will meet the requirements is to write data to Amazon ElastiCache. This way, the application can write session data to a fast, scalable, and reliable in-memory data store that can be served reliably across multiple requests. The other options either involve writing data to persistent storage, which is slower and more expensive than in-memory storage, or writing data to the root filesystem, which is not shared among multiple EC2 instances.

This solution meets the requirements because it provides a caching layer that can store and retrieve encrypted data from multiple nodes. Amazon ElastiCache for Redis supports encryption at rest and in transit, and can scale horizontally to increase the cache capacity and availability. Amazon ElastiCache for Memcached does not support encryption, Amazon CloudFront is a content delivery network that is not suitable for caching database queries, and Amazon DynamoDB Accelerator (DAX) is a caching service that only works with DynamoDB tables.

This solution will meet the requirements by using AWS Serverless Application Model (AWS SAM) templates and gradual deployments to automatically test the Lambda functions. AWS SAM templates are configuration files that define serverless applications and resources such as Lambda functions. Gradual deployments are a feature of AWS SAM that enable deploying new versions of Lambda functions incrementally, shifting traffic gradually, and performing validation tests during deployment. The developer can enable gradual deployments through AWS SAM templates by adding a DeploymentPreference property to each Lambda function resource in the template. The developer can set the deployment preference type to Canary10Percent30Minutes, which means that 10 percent of traffic will be shifted to the new version of the Lambda function for 30 minutes before shifting 100 percent of traffic. The developer can also use hooks to test the deployment, which are custom Lambda functions that run before or after traffic shifting and perform validation tests or rollback actions.

The solution that will meet the requirements with the most operational efficiency is to create a new CodePipeline stage that occurs after the container image is built. Configure ECR basic image scanning to scan on image push. Use an AWS Lambda function as the action provider. Configure the Lambda function to check the scan results and to fail the pipeline if there are findings. This way, the container image is analyzed earlier in the CI/CD pipeline and any vulnerabilities are detected and reported before deploying to the EKS cluster. The other options either delay the analysis until after deployment, which increases the risk of exposing insecure images, or perform analysis on the source code instead of the container image, which may not capture all the dependencies and configurations that affect the security posture of the image.

AWS CodeCommit is a service that provides fully managed source control for hosting secure and scalable private Git repositories. The development team can use CodeCommit to store the program code and prepare for the CI/CD pipeline. CodeCommit integrates with other AWS services such as CodePipeline, CodeBuild, and CodeDeploy to automate the code build and deployment process.

Requirement Summary:

Users experiencebuffering/delaywhen starting video stream

Architecture:

CloudFront → API Gateway → Lambda → DynamoDB

Need toidentify root causeof performance issues

Evaluate Options:

✅A: Enable AWS X-Ray tracing

✅Ideal forend-to-end tracing

Visualizeslatency across services (API Gateway, Lambda, DynamoDB)

Creates aservice mapfor easy identification of bottlenecks or errors

Designed specifically fordistributed tracingand performance monitoring

B: CloudWatch Logs Insights

⚠️Helpful for querying logs

But lacks thevisual trace linkageacross services like X-Ray

Does not identifywhere latency accumulates

C: AWS Config

❌Tracks configuration changes,not runtime performance

D: CloudTrail + CloudWatch Logs

❌More useful foraudit/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

Why Option B is Correct:Amazon ECS does not natively process docker-compose.yml files. Instead, the configurations from docker-compose.yml must be converted into ECS-compatible configurations within a task definition. Task definitions are the primary way to specify container configurations in ECS, including service dependencies like databases, caches, and volumes.

Steps to Resolve the Error:

Extract the configurations from the docker-compose.yml file.

Map the dependencies and settings to the appropriate ECS task definition fields.

Deploy the task definition to the ECS cluster.

Why Other Options are Incorrect:

Option A:Docker labels do not directly impact ECS task execution or integrate with ECS service configurations.

Option C:ECS namespaces do not exist as a feature.

Option D:Changing the service type to REPLICA does not resolve the issue of missing service dependency configurations.

AWS Documentation References:

Amazon ECS Task Definitions

Migrating Docker Compose Workloads to ECS

S3 Object Lambda allows you to add your own code to process data retrieved from S3 before returning it to an application. You can use an AWS Lambda function to modify the data, such as removing PII, redacting confidential information, or resizing images. You can create an S3 Object Lambda access point and associate it with your Lambda function. Then, you can use the access point to request objects from S3 and get the modified data back. This way, you can maintain only one copy of the original document in S3 and apply different transformations depending on who accesses it. Reference: Using AWS Lambda with Amazon S3