Amazon Web Services DVA-C02 Exam With Confidence Using Practice Dumps

The correct answer is D because the current DynamoDB table design is causing a hot partition problem. The table uses order_date as the partition key, which means that during peak ordering periods, a very large number of writes are likely targeting the same partition key value for the current date. In DynamoDB, write throttling often occurs when traffic is not evenly distributed across partition keys, even when the table uses on-demand mode . On-demand capacity can automatically scale, but it still depends on proper key distribution to avoid concentrated activity on a small number of partitions.

By changing the partition key to customer_id and using order_id as the sort key, writes will be spread across many customers instead of being concentrated on a small number of date values. This creates a more balanced write pattern and better aligns with DynamoDB best practices for high-scale workloads. DynamoDB documentation emphasizes choosing partition keys with high cardinality and even request distribution to avoid throttling and improve performance.

Option A is not a good design because a sequential partition key can still create uneven access patterns and usually does not support meaningful access requirements. Option B is not the best answer because simply increasing capacity does not fix a poor partition key design; hot partitions can still throttle. Option C is unnecessary because the issue is not that DynamoDB is the wrong service, but that the table schema needs redesign.

Therefore, the best solution is to refactor the key schema so that writes distribute more evenly across partitions, which is exactly what D achieves.

Comprehensive and Detailed Step-by-Step Explanation:

To confirm that the HTTP headers, body, and responses meet expectations, you need to test the specific HTTP Task state in isolation and inspect the details.

Option A: TestState API with TRACE:

The TestState API allows developers to test individual states in a state machine without executing the entire workflow.

Setting the inspection level to TRACE provides detailed information about the HTTP request and response, including headers, body, and status codes.

This option provides the precise and granular testing required to verify the HTTP Task functionality.

Why Other Options Are Incorrect:

Option B: The DEBUG inspection level provides less detailed information than TRACE and focuses on general debugging, not a detailed view of HTTP interactions.

Option C: Step Functions does not have a " data flow simulator " to test individual tasks; this option is not valid.

Option D: Changing the state machine’s log level to ALL increases logging granularity for the entire state machine but does not allow isolated testing of a specific HTTP Task.

Configuring the Lambda function to use ephemeral storage and processing data in the /tmp directory improves performance by leveraging local storage during execution.

Why Option C is Correct:

Ephemeral Storage: Lambda provides temporary storage (up to 10 GB) in the /tmp directory for each invocation, which is faster than pulling data directly from S3 multiple times.

Performance Boost: Data can be downloaded to /tmp, processed locally, and uploaded to the destination S3 bucket, minimizing S3 network calls.

Low Overhead: This approach requires only minimal changes to the function ' s configuration.

Why Not Other Options:

Option A: Using Amazon EFS adds complexity and is unnecessary for this use case.

Option B: Scheduling the function does not address the root cause of slow performance.

Option D: Lambda layers improve deployment efficiency, not runtime performance for this scenario.

Using Ephemeral Storage in AWS Lambda

Best Practices for S3 and Lambda Performance