Amazon Web Services DVA-C02 - AWS Certified Developer - Associate

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

Step Functions Retriers: Retriers provide a built-in way to gracefully handle transient errors within State Machines. Here's how to use them:

Directly attach a retrier to the problematic 'GetResource' task.

Configure the retrier:

ErrorEquals: Set this to ['TooManyRequestsException'] to target the specific error.

IntervalSeconds: Set to 10 for the desired retry delay.

MaxAttempts: Set to 1, as you want only one retry attempt.

Error Handling:

Upon 'TooManyRequestsException', the retrier triggers the task again after 10 seconds.

On a second failure, Step Functions moves to the next state or fails the workflow, as per your design.

'IllegalArgumentException' causes error propagation as intended.

Comprehensive and Detailed Explanation (AWS Verified – 250–300 Words)

When an Amazon S3 bucket is configured with default encryption using SSE-KMS, AWS Key Management Service (KMS) plays an active role in every PutObject request. Although the IAM user already has the s3:PutObject permission, this alone is not sufficient when SSE-KMS is enabled.

According to AWS documentation, when a client uploads an object to an S3 bucket encrypted with SSE-KMS, Amazon S3 must call AWS KMS on behalf of the caller to generate a unique data encryption key. This operation requires the kms:GenerateDataKey permission on the KMS key used for encryption. If this permission is missing, the PutObject request fails with an Access Denied error—even though S3 permissions appear correct.

AWS explicitly states that IAM principals uploading objects to SSE-KMS–encrypted buckets must be allowed to use the KMS key. At a minimum, the following KMS permissions are required:

kms:GenerateDataKey

(Implicitly) access allowed by the KMS key policy

Option C correctly resolves the issue by granting the IAM user permission to generate a data key via AWS KMS, enabling successful encryption during the upload process.

Option A is invalid because s3:EncryptionConfiguration is not required for object uploads.

Option B is unnecessary because the IAM user already has s3:PutObject.

Option D is incorrect because ACLs are not used to control KMS encryption permissions and are discouraged by AWS best practices.

AWS Systems Manager Parameter Store is a service that provides secure, hierarchical storage for configuration data management and secrets management. The developer can update the application to retrieve the variables from Parameter Store by using the AWS SDK or the AWS CLI. The developer can use unique paths in Parameter Store for each variable in each environment, such as /dev/api-url, /test/api-url, and /prod/api-url. The developer can also store the credentials in AWS Secrets Manager, which is integrated with Parameter Store and provides additional features such as automatic rotation and encryption.

The X-Ray daemon is a software that collects trace data from the X-Ray SDK and relays it to the X-Ray service. The X-Ray daemon can run on any platform that supports Go, including Linux, Windows, and macOS. The developer can install and run the X-Ray daemon on the on-premises servers to capture and relay the data to the X-Ray service with minimal configuration. The X-Ray SDK is used to instrument the application code, not to capture and relay data. The Lambda function solutions are more complex and require additional configuration.

To begin using AWS X-Ray, the team must (1) instrument the application to emit trace segments/subsegments and (2) ensure there is a component that can send trace data to the X-Ray service.

Instrumenting code is typically done with the AWS X-Ray SDK (language-specific). This adds tracing to inbound requests and downstream calls, creates segments/subsegments, and attaches annotations/metadata. That corresponds to Option C.

For many compute environments (especially EC2, ECS on EC2, and on-prem servers), the application sends trace data via the X-Ray daemon/agent running locally. The daemon batches segments and forwards them to the X-Ray service endpoint. That corresponds to Option D.

Option B is helpful after traces exist (using the X-Ray console or CloudWatch ServiceLens), but it is not a prerequisite to “begin using” X-Ray.

Option A is unrelated. X-Ray does not require SQS for instrumentation output.

Option E is incorrect because X-Ray stores trace data in its managed backend; you do not create a DynamoDB table for trace logs.

Therefore, the team needs to instrument the code with the X-Ray SDK and install/run the X-Ray agent/daemon on the servers where the application runs.