DVA-C02 Exam Dumps : AWS Certified Developer - Associate

The company already has SSM Agent deployed on all servers, which enables centralized, scalable fleet operations without logging in to each instance. The most operationally efficient solution is to automate key distribution using Systems Manager Run Command, executing a script across the entire fleet (or a targeted subset) in one action.

Option B uses S3 as a simple distribution source and Run Command to perform the update at scale. The developer can store the required key material in a controlled S3 location and write a script that places the key in the correct filesystem path (with correct permissions/ownership), updates authorized_keys if needed, and restarts any services if required. Run Command can target instances by tags, resource groups, or instance IDs and provides execution logging and status.

Option A is not operationally efficient because it requires logging into each server manually.

Option C and D push the distribution burden to humans (“grant team members permissions to download”), which is error-prone, slow, and not scalable. Also, distributing private keys broadly to individuals increases risk.

This solution meets the requirements in the most operationally efficient manner because it does not require any infrastructure provisioning or management. The developer can create a Lambda function that makes the API call and configure an EventBridge rule that triggers the function once a day at a designated time. This is a serverless solution that scales automatically and only charges for the execution time of the function.

This workload is overwhelmingly read-heavy and repeatedly queries the same relatively static reference data (postal codes mapped to coordinates). The best way to reduce latency and offload Amazon RDS is to introduce an application-side cache using a lazy loading (cache-aside) pattern and choose a TTL that reflects how often the underlying data changes.

With lazy loading , the application first checks the cache for the postal code. If the value is present (a cache hit), the application returns the coordinates immediately with low latency and without querying the database. If the value is absent (a cache miss), the application reads from RDS, returns the result, and then populates the cache for subsequent requests. This pattern is operationally simple and widely used because the application controls what is cached and when.

A large TTL is appropriate here because postal-code-to-coordinate mappings typically do not change frequently. A longer TTL maximizes cache hit ratio, which is critical because each postal code is requested thousands of times per day. High cache hit rates reduce database read load, decrease query contention, and substantially improve response times. In contrast, a small TTL would cause frequent expirations and repeated database lookups, reducing the performance benefit and increasing database load.

Write-through and write-behind caching (options C and B) are designed to coordinate caching with writes . They are useful when write consistency and write performance are central concerns. Here, writes are rare; the performance issue is repeated reads. Write-behind can also introduce consistency delays, which is unnecessary for this scenario.

Therefore, the best change is D : implement a lazy loading (cache-aside) caching strategy with a large TTL to keep hot, rarely changing postal code lookups in cache and dramatically reduce read latency and database load.