AWS Certified Developer Associate DVA-C02 Practice Question
A development team is refining their serverless application's logging approach to enable better insight during debug sessions. They need the capability to effortlessly filter and search through log entries for specific execution contexts like the runtime version and unique request identifiers. Which logging strategy should the team employ to ensure their log data is conducive to rapid and effective troubleshooting?
Depend solely on a distributed tracing service to monitor the application, presuming it alleviates the need for detailed logs.
Adopt structured logging with a consistent format like JSON that supports key-value pairs for all pertinent information, enhancing the interpretability and searchability of the logs.
Use a rudimentary logging mechanism that captures standard output streams without specific structure to save on storage space.
Focus on capturing only exception stack traces in the log files, considering that this information suffices for pinpointing issues.
Structured logging is an approach where developers log data in a consistent, predefined format, like JSON with key-value pairs. This format is beneficial because it makes it easier to search, filter, and analyze log data, allowing teams to isolate issues rapidly and efficiently. The addition of structured contextual information such as execution runtime version and unique identifiers facilitates targeted querying which is highly valuable during troubleshooting. Alternatives like unstructured text logs, only capturing error messages, or sole reliance on distributed tracing services without context-rich application-specific logs would not provide the same ease of filtering and detail required for efficient debugging.
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AWS Certified Developer Associate DVA-C02
Troubleshooting and Optimization
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