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Answer: Increase the memory allocated to the Lambda function, as it is directly proportional to the CPU and network performance.
Option A is the correct answer. When optimizing the performance of a data transformation job running on AWS Lambda, increasing the memory allocated to the function can improve CPU and network performance, as they are directly proportional. Allocating more memory can help handle larger data sets and reduce the execution time of the function. However, it's essential to find the right balance between memory allocation and cost. Option B may not necessarily improve performance, as decreasing the timeout setting could lead to premature termination of the function. Option C can be useful for complex workflows but may not directly impact the performance of a single data transformation job. Option D can be beneficial for specific use cases but may not be applicable to all data transformation tasks.
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You are tasked with optimizing the performance of a data transformation job that is running on AWS Lambda. Which of the following strategies would you implement, and why?
A
Increase the memory allocated to the Lambda function, as it is directly proportional to the CPU and network performance.
B
Decrease the timeout setting of the Lambda function to reduce the execution time and cost.
C
Use AWS Step Functions to orchestrate multiple Lambda functions, allowing for parallel processing and better resource utilization.
D
Implement caching mechanisms to store intermediate results and reduce the need for repeated data processing.