AWS Certified Developer Associate DVA-C02 Practice Question
A development team is monitoring a serverless function that handles data processing tasks triggered by a NoSQL database stream. They are encountering issues with failed executions and need a way to quantify these incidents due to client-related mistakes. Which approach would BEST assist them in identifying and quantifying these specific issues over time for the purpose of application fine-tuning?
Increase the function's monitoring level to capture all execution paths and errors
Implement a custom metric recorded directly from the function whenever a client error is encountered during the data processing
Incorporate a distributed tracing system to tag and review instances of client errors in function executions
Expand log entries to include detailed error messages for each execution and periodically review for client errors
Creating and emitting a custom metric when the serverless function encounters client-related errors allows the team to track the exact number of such incidents. This is because custom metrics can be tailored to the application's unique operational requirements, thus offering the most insightful data for this particular situation. Options like enabling detailed monitoring or reviewing logs would provide broader information that might not be directly related to the client-related errors. Similarly, while distributed tracing systems could give further insights into the execution path, they are generally not as suitable for aggregating and analyzing event counts over time specifically for optimization purposes.
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AWS Certified Developer Associate DVA-C02
Troubleshooting and Optimization
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