DVA-C02 Exam Dumps - AWS Certified Developer - Associate

Using a dead-letter queue (DLQ) with Amazon SQS is the most operationally efficient solution for handling unprocessable messages.

Amazon SQS Dead-Letter Queue:

A DLQ is used to capture messages that fail processing after a specified number of attempts.

Allows the application to continue processing other messages without being blocked.

Messages in the DLQ can be analyzed later for debugging and resolution.

Why DLQ is the Best Option:

Operational Efficiency: Automatically defers messages with errors, ensuring the queue is not blocked.

Analysis Ready: Messages in the DLQ can be inspected to identify recurring issues.

Scalable: Works seamlessly with Lambda and SQS at scale.

Why Not Other Options:

Option A: Logs the messages but does not resolve the queue blockage issue.

Option C: FIFO queues and 0-second retention do not provide error handling or analysis capabilities.

Option D: Alerts administrators but does not handle or store the unprocessable messages.

Steps to Implement:

Create a new SQS queue to serve as the DLQ.

Attach the DLQ to the primary queue and configure the Maximum Receives setting.

Using Amazon SQS Dead-Letter Queues

Best Practices for Using Amazon SQS with AWS Lambda

Why Option B is Correct:A customer-managed AWS Key Management Service (KMS) key allows for encryption at rest and provides the ability to rotate the key on demand. This ensures compliance with security requirements for key management and database encryption.

RDS integrates natively with AWS KMS, allowing the use of a customer-managed key for encrypting data at rest.

Key rotation can be managed directly in AWS KMS without needing custom solutions.

Why Other Options are Incorrect:

Option A: AWS KMS managed encryption keys (AWS-owned keys) do not support key rotation on demand.

Option C & D: Storing keys in AWS Secrets Manager with custom rotation is not a recommended approach for database encryption. AWS KMS is designed specifically for secure key management and encryption.

AWS Documentation References:

Encrypting Amazon RDS Resources

AWS Key Management Service (KMS)

These solutions will mitigate the error most cost-effectively because they do not require increasing the provisioned throughput of the DynamoDB table or creating additional resources. Exponential backoff is a retry strategy that increases the waiting time between retries to reduce the number of requests sent to DynamoDB. The AWS SDKs for DynamoDB implement exponential backoff by default and also provide other features such as automatic pagination and encryption. Increasing the read and write throughput of the DynamoDB table, creating a DynamoDB Accelerator (DAX) cluster, or creating a second DynamoDB table will incur additional costs and complexity.

The requirements are: (1) store an API key that a Lambda function will use, (2) ensure the secret is encrypted at rest using AWS KMS, and (3) the company must control key rotation, which implies using a customer managed KMS key (CMK) rather than an AWS managed key.

Option C meets the requirements by storing the API key in AWS Systems Manager Parameter Store as a SecureString parameter encrypted with a customer managed KMS key. SecureString is designed for sensitive configuration data and integrates with KMS so the organization can choose the CMK, manage its lifecycle, and control rotation policies. The Lambda function can retrieve the parameter at runtime using the AWS SDK, and IAM policies can tightly control access to the parameter and to the KMS key.

Option E also meets the requirements by storing the API key in a Lambda environment variable encrypted with a customer managed KMS key. Lambda encrypts environment variables at rest and allows you to specify a customer managed KMS key for encryption. This gives the company control over key rotation and key policy, satisfying the “must control key rotation” requirement. The function reads the value from the environment at runtime without additional network calls.

Why the other options fail:

A uses an AWS managed KMS key, which does not satisfy the requirement for the company to control rotation (you cannot manage rotation of AWS managed keys in the same way).

B is a plain String parameter, which is not encrypted as a secret and does not meet the at-rest encryption requirement.

D uses an AWS managed KMS key, again failing the company-controlled rotation requirement.

Therefore, the two valid solutions are C (Parameter Store SecureString with a customer managed CMK) and E (Lambda environment variable encrypted with a customer managed CMK).

https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html

The workload is a classic “hot key / hot dataset” pattern: many reads repeatedly target a small subset of items. The best way to improve performance and reduce latency is to add a caching layer so the application does not hit DynamoDB for every read.

Option B is purpose-built for DynamoDB read acceleration: DynamoDB Accelerator (DAX) is an in-memory cache that sits in front of DynamoDB and is API-compatible for many DynamoDB operations. DAX can dramatically reduce read latency (often to microseconds) for frequently accessed items and offload read traffic from the table.

Option A can also help: ElastiCache (Redis/Memcached) can be used as an application-managed cache. This is useful when the application wants more control over caching strategy, TTLs, and non-DynamoDB data caching. For ECS applications, ElastiCache is a common high-performance caching choice.

Why not the others:

C (strongly consistent reads) typically increases latency and capacity consumption; it does not improve performance for repeated reads.

D (increase read capacity) can reduce throttling, but it does not reduce latency as effectively as caching and can be more expensive for hot-read patterns.

E (adaptive capacity) helps DynamoDB handle uneven workloads, but it is not a direct performance boost like caching for repeated reads of a small dataset.

Therefore, adding caching via ElastiCache and/or DAX best improves performance. With “select two,” A and B are correct.

Comprehensive and Detailed Step-by-Step Explanation:

Option D: AWS AppConfig with CloudWatch Application Signals

AWS AppConfig is designed for managing and deploying application configurations dynamically, without redeployment.

CloudWatch Application Signals provide automatic rollback mechanisms in case of an unhealthy application state due to bad configuration changes.

This solution meets the requirements with minimal operational overhead by ensuring both dynamic updates and rollback functionality.

Why Other Options Are Incorrect:

Option A and B: AWS Secrets Manager is designed for secrets management, not dynamic application configuration. Custom Config rules add unnecessary complexity.

Option C: While CloudWatch alarms can monitor application changes, using alarms for rollback requires manual setup and lacks the automatic rollback provided by Application Signals.