AWS Certified Developer Associate DVA-C02 Practice Question
A software team has noticed that a serverless function, which processes incoming streaming data, is occasionally hitting its maximum execution time, resulting in incomplete tasks. The function's workload involves complex computations and varying sizes of data payloads. To enhance the processing efficiency and reduce the likelihood of timeouts, which of the following adjustments should be the team's initial step?
Extend the maximum allowed execution time for the function.
Allocate more memory to the serverless function.
Manually optimize the function by removing lines of code to decrease the execution time.
Implement a distributed tracing service to examine the function's behavior in detail.
Allocating more memory to the serverless function can lead to an improvement in the compute capacity available to the function. Since the compute capacity scales with the memory size, this can decrease execution time for complex tasks and large data payloads. Thus, timeouts are less likely to occur. Extending the function's execution time may alleviate symptoms but does not address performance inefficiency and may increase costs without solving the real issue. Removing code lines is a practice of code optimization, but without a strategy, it can be ineffective at solving the problem. Moreover, it is not a configuration setting that can be adjusted for performance tuning. Diagnostic tracing with a tool can highlight performance issues, but it does not directly improve execution time.
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Why does allocating more memory to a serverless function improve performance?
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What happens if you just extend the execution time of a serverless function?
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How does distributed tracing help with serverless functions?
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AWS Certified Developer Associate DVA-C02
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