A company has built an AWS Lambda function to convert large image files into output files that can be used in a third-party viewer application. The company recently added a new module to the function

Question

Options

• AUse AWS CodeDeploy to deploy the function code.
• BUse Lambda layers to package and load dependencies.
• CIncrease the memory size of the function.
• DUse Amazon S3 to host the function dependencies.

• A
  3% (1)
• B
  78% (29)
• C
  5% (2)
• D
  14% (5)

Explanation

Lambda Layers (B) solve this directly: by extracting large dependencies into a separate layer, the main function package stays small, which means faster uploads and deployments when you update only the function code - the layer is already cached and doesn't need to be re-uploaded each time.

A is wrong because CodeDeploy manages traffic shifting and rollout strategies (canary, linear deployments), not bundle size or upload speed - it doesn't make a large package smaller or faster to transfer.

C is wrong because increasing memory affects runtime execution performance, not deployment time; the package still has to be uploaded at full size regardless of how much memory the function has.

D is wrong because Lambda function code must be packaged and deployed through Lambda's own mechanism - you can reference S3 for the ZIP artifact, but that doesn't reduce bundle size or meaningfully speed up deployment compared to using layers.

Memory tip: Think of layers as shared shelves - you put the heavy books (dependencies) on the shelf once, and only carry your thin notebook (function code) each time you need to make a change. Smaller package = faster deployment.

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