A company has a monolithic desktop-based application that processes images.

Lambda does not allow the developer to select an EC2 instance type, so option A is invalid. For Lambda, CPU allocation scales proportionally with memory. If the image-processing workload is CPU-bound or memory-constrained, increasing the configured memory can also increase available CPU power and reduce execution duration. Updating the Python runtime may provide minor improvements but is not the primary Lambda performance control. Reserved concurrency controls how many concurrent executions are reserved for the function; it does not make one invocation process faster. The issue is that each run takes too long, not that the function lacks concurrency. AWS Lambda documentation confirms that increasing function memory also increases CPU allocation, and memory can be tuned to improve performance. ( AWS Documentation )

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