A developer is building an application on AWS.

In AWS Lambda, CPU power scales proportionally with the memory configuration. Lambda does not let you directly set “CPU cores” as a standalone setting in the general case; instead, increasing the function’s configured memory increases the CPU allocation (and other resources such as network throughput) available to the function. Therefore, to reduce latency caused by insufficient CPU, the developer should increase the function’s memory setting.

Option B directly addresses the CPU limitation in the supported Lambda configuration model. This is a common performance tuning approach: raise memory, benchmark, and find the optimal cost/performance point.

Option A is not the right lever for most Lambda functions because Lambda compute is configured via memory (and architecture), not by directly raising a “vCPU quota” per function in a typical tuning workflow.

Option C increases /tmp storage capacity and helps when dealing with large temporary files, not CPU availability.

Option D increases the maximum runtime allowed per invocation, but it does not give the function more CPU and will not reduce latency caused by CPU starvation.

Therefore, increasing the allocated memory is the correct way to increase CPU for a Lambda function.