DVA-C02 Exam Dumps - AWS Certified Developer - Associate

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.

The HTTP header that the developer should use for this analysis is the X-Forwarded-For header. This header contains the IP address of the client that made the request to the Application Load Balancer. The developer can use this header to analyze patterns for the client IP addresses that use the application. The other headers either contain information about the protocol, host, or port of the request, which are not relevant for the analysis.

The Immutable deployment method is the best choice when a company requires minimal downtime and automatic rollback in case of a failure. Here ' s why:

In Immutable deployments, a new set of instances is launched with the updated version. These instances are tested and validated before they replace the old instances. This ensures zero downtime and immediate rollback if the deployment fails.

All at once (A) causes downtime because the update replaces all instances simultaneously.

Rolling deployments (B) update a few instances at a time, but if a failure occurs midway, downtime or partial unavailability can happen.

Snapshots (C) are not a deployment strategy in Elastic Beanstalk.

Why Option B is Correct:Setting the visibility timeout ensures that once a message is being processed, it is temporarily hidden from other consumers. The Lambda function must delete processed messages to avoid re-processing when the visibility timeout expires.

Why Other Options are Incorrect:

Option A: Message retention affects how long messages stay in the queue, not how they are processed.

Option C: Receive message wait time optimizes long polling but does not prevent re-processing.

Option D: Delivery delay introduces latency for new messages and does not address message re-processing.

AWS Documentation References:

Using SQS Visibility Timeout

The correct answers are B and E .

The existing platform already runs on Kubernetes , so the fastest and most compatible migration path is to use Amazon EKS , which is AWS’s managed Kubernetes service. This preserves the application’s orchestration model and minimizes refactoring. Because the clusters must be highly available , EKS is a strong fit since it is designed to run Kubernetes workloads across multiple Availability Zones.

For shared storage, the on-premises application currently depends on a common NFS share that all containers can access. On AWS, the service that most closely matches this requirement is Amazon EFS , which provides a managed, elastic, shared file system that supports NFS access and can be mounted concurrently from multiple compute nodes. By contrast, Amazon EBS is block storage that is generally attached to a single instance and is not the right replacement for a shared multi-node NFS workload.

Therefore, B is correct because it moves the shared NFS data to Amazon EFS and stores the container images in Amazon ECR , which is the proper AWS managed registry for container images. E is correct because it runs the applications on Amazon EKS and uses Amazon EFS as the shared mounted file system across the Kubernetes worker nodes.

Option C is wrong because it uses ECS , not Kubernetes. Option D is wrong because EBS does not meet the requirement for a shared NFS-like file system across highly available Kubernetes nodes. Option A is wrong for the same storage reason.

So, the best migration combination is Amazon EFS + Amazon EKS , which makes B and E correct.

The correct answer is B because AWS AppConfig is designed specifically for application configuration, feature flags, controlled rollouts, configuration validation, and safe deployment practices . The scenario requires rollout in staging first, then gradual exposure to 10% of production users , audience targeting by user attributes , validation before deployment, and the ability to perform instant rollback without redeploying code . AWS AppConfig feature flags provide all of these capabilities.

AWS documentation describes AppConfig as a service for creating, managing, and deploying dynamic application configurations. With feature flags, a company can separate release from deployment and safely enable or disable features for selected audiences. AppConfig supports environment-scoped configurations , so staging and production can have different flag states. It also supports deployment strategies for gradual rollouts and includes validators to check configuration before deployment. In addition, AppConfig supports immediate rollback if a problem occurs, which is a major advantage over approaches that depend on application redeployment.

Option A is incorrect because environment variables typically require a deployment change and do not provide audience targeting, staged rollout controls, or fast rollback. Option C is better suited for storing configuration values, but it does not provide the same rich feature flag targeting and rollout management capabilities as AppConfig. Option D is possible in theory, but it requires building and maintaining a custom service, which is more complex and less reliable than using the managed AWS service built for this purpose.

Therefore, AWS AppConfig feature flags are the best fit for the stated requirements.

Because the Lambda function is successfully triggered by the S3 event notification, the invocation path (S3 → Lambda) is working correctly. The failure occurs specifically when the function tries to write to DynamoDB, which strongly indicates an authorization problem rather than an invocation, scaling, or infrastructure issue.

In AWS, a Lambda function interacts with other services by using its execution role (an IAM role). AWS documentation explains that a Lambda function must have explicit IAM permissions to call downstream services such as DynamoDB. To write items, the role typically needs actions like dynamodb:PutItem (and sometimes dynamodb:UpdateItem, dynamodb:BatchWriteItem, depending on code behavior) on the target table resource ARN. If these permissions are missing or scoped incorrectly, DynamoDB returns an AccessDeniedException (or similar) and the function fails at the write step.

Option A is unlikely because exceeding concurrency would typically prevent invocation or cause throttling at the Lambda service level, not selectively fail only at DynamoDB write time after the function begins executing.

Option B is incorrect: DynamoDB does not require a GSI to support writes. GSIs are for alternate query access patterns, not mandatory for write operations.

Option D is incorrect because DynamoDB is a regional service, not tied to a single Availability Zone, and Lambda does not need to be “in the same AZ” to access it.

Therefore, the most likely cause is that the Lambda execution role lacks the necessary IAM permissions to perform DynamoDB write operations.