A company’s AWS Lambda function is experiencing performance issues. The Lambda function performs many CPU-intensive operations. The Lambda function is not running fast enough and is creating bottlenecks in the system. What should a SysOps administrator do to resolve this issue?
Select an option, then click Submit answer.
Reference / correct answer:
Increase the amount of memory for the Lambda function.
Most accepted answer: C. Increase the amount of memory for the Lambda function.
Community votes: C=5
Selected Answer: C Ans is C: https://docs.aws.amazon.com/lambda/latest/dg/configuration-function-common.html#configuration-memory-console upvoted 7 times
Selected Answer: C Lambda is aws managed but memory can be increased by user/service subscriber upvoted 1 times
Selected Answer: C Lambda is AWS managed server, thus cannot choose CPU, but can add Memory. upvoted 3 times
Selected Answer: C Option A (In the CPU launch options for the Lambda function, activate hyperthreading) is not a valid option because AWS Lambda manages the underlying infrastructure, including the CPU configuration. SysOps administrators do not have direct access to adjust hyperthreading settings for Lambda functions. Option B (Turn off the AWS managed encryption) is unrelated to the performance issue caused by CPU-intensive operations. AWS managed encryption refers to the automatic encryption of data at rest for Lambda function code and other resources. Disabling encryption won't improve the Lambda function's performance. upvoted 3 times LudiVoss 2 years ago Hey are you prepping for a license right now? I see you killing it in these comments, you sure go in detail. upvoted 1 times Yowie351 1 year, 11 months ago Chat GPT mate... upvoted 1 times ... ... Christina666 2 years, 6 months ago Option C (Increase the amount of memory for the Lambda function) is the correct choice. AWS Lambda allows you to configure the amount of memory allocated to a function, and this has a direct impact on the available CPU power. When you increase the memory, Lambda automatically allocates proportional CPU power, which can significantly improve the performance of CPU-intensive operations. The more memory you allocate, the more CPU power the function receives, leading to faster execution times. Option D (Load the required code into a custom layer) is unrelated to addressing the performance issues caused by CPU-intensive operations. Custom layers in AWS Lambda are used to share code libraries and dependencies among multiple functions. While it can be helpful for code reuse and organization, it won't directly resolve the CPU bottleneck issue. upvoted 4 times ...
Hey are you prepping for a license right now? I see you killing it in these comments, you sure go in detail. upvoted 1 times Yowie351 1 year, 11 months ago Chat GPT mate... upvoted 1 times ...