DVA-C02 Exam Dumps - AWS Certified Developer - Associate

For asynchronous Lambda invocations , AWS provides built-in failure handling options that require minimal code and minimal operational work. When an async invocation fails (after Lambda’s internal retry behavior), the event can be sent to a dead-letter queue (DLQ) so it is not lost. A DLQ is the standard mechanism for capturing events that could not be processed successfully, preserving the original payload for later inspection and reprocessing.

Using a DLQ (typically Amazon SQS or Amazon SNS ) gives durable storage of failed events and decouples failure recovery from the main execution path. The developer can later re-drive the messages by building a simple replay process, such as moving messages from the DLQ back to the original event source or invoking the function again with the stored payloads. This aligns with the requirement: “store messages that resulted in failed invocations so the application can retry later.”

Among the options, C is the only one that uses Lambda’s native async failure capture mechanism. Options A and B introduce unnecessary complexity and do not reliably store the original failed invocation payloads as a first-class workflow (CloudWatch Logs is not a message queue, and EventBridge→SNS doesn’t automatically capture only failed events from Lambda async invocations). Option D changes the architecture to an SQS pull model for the Lambda function; while valid, it is more than needed and adds design/operational considerations (polling, batching, visibility timeouts, DLQ configuration on the queue, etc.) compared with simply enabling DLQ support for async invokes.

Therefore, the least operational overhead solution is C : configure a dead-letter queue for the Lambda function’s asynchronous invocations so failed events are stored for later retry.

Amazon DynamoDB performance depends heavily on partition key design . AWS documentation warns against access patterns that repeatedly target the same partition key, as this causes hot partitions , even in on-demand mode.

In this scenario, most reads focus on the current day’s forecast for a small number of large cities. Using CityId alone as the partition key would cause hot partitions for those cities. Adding a calculated suffix (such as a hash or modulo value) to CityId spreads read requests across multiple partitions while still allowing efficient querying.

Using ForecastDate as the partition key (Options C and D) would concentrate traffic on a single value (today’s date), creating severe hot partition issues. Option B lacks a meaningful access pattern and complicates queries.

AWS documentation explicitly recommends key-suffix techniques for workloads with skewed access patterns to distribute traffic evenly. Therefore, using CityId with a calculated suffix as the partition key is the correct design.

Amazon CloudWatch Logs Insights is a purpose-built log analytics tool that allows developers to run interactive queries directly against CloudWatch Logs. AWS documentation highlights Logs Insights as the fastest and most efficient way to search, filter, and analyze log data without exporting it.

Logs Insights queries can filter error messages, extract timestamps, and aggregate failure counts over specific time windows. This directly satisfies the requirement to identify when intermittent failures occurred and what errors caused them.

Manual browsing (Option A) and local downloads (Option C) are time-consuming and do not scale. Exporting logs to S3 and querying with Athena (Option D) introduces unnecessary infrastructure and operational overhead.

Therefore, CloudWatch Logs Insights is the most operationally efficient solution.

https://aws.amazon.com/blogs/devops/automated-code-review-on-pull-requests-using-aws-codecommit-and-aws-codebuild/