DVA-C02 Exam Dumps : AWS Certified Developer - Associate

The correct answer is C because for an in-place deployment on Amazon EC2 instances, AWS CodeDeploy automatic rollback works by redeploying the last known good revision when a deployment fails or when a configured monitoring alarm is triggered. AWS documentation explains that when automatic rollback is enabled, CodeDeploy creates a new deployment that uses the most recent successfully deployed application revision. This rollback deployment receives its own new deployment ID , because it is treated as a separate deployment event rather than a simple undo operation.

Option A is incorrect because CodeDeploy does not restore an Amazon S3 snapshot of the previous deployment state for EC2 in-place deployments. It redeploys the last successful revision instead. Option B describes behavior associated with blue/green deployment patterns , not in-place deployments . Route 53 alias switching and blue/green environment replacement are not how CodeDeploy handles failed validation during an EC2 in-place deployment. Option D is incorrect because CodePipeline orchestrates stages in a delivery pipeline, but it is not the service that performs the rollback behavior described here.

This distinction is important in AWS deployment design. In-place deployments update the same EC2 instances already serving the application. If validation fails, CodeDeploy does not roll traffic back through DNS or infrastructure replacement. Instead, it launches a rollback deployment of the previously successful application revision across those same instances. That behavior directly aligns with AWS CodeDeploy automatic rollback functionality for EC2/On-Premises in-place deployments.

Therefore, the correct result is that CodeDeploy redeploys the last known stable version as a new deployment , which makes C the right answer.

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.