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Answer: Utilizing an Amazon EFS file system to store and mount the library for all Lambda functions.
The correct answer is D. Lambda layers have a size limitation of 250 MB, which is insufficient for a 15 GB library. Downloading the library from Amazon S3 each time would significantly increase the function's execution time. Creating a Lambda container image is cumbersome for a continuously growing library. Amazon EFS can handle large, growing libraries, and can be mounted on multiple Lambda functions, providing a scalable and efficient solution.
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How can a developer provide access to a growing 15 GB custom ML library for AWS Lambda functions?
A
Using Lambda layers to include the library and attaching these layers to functions.
B
Storing the library in Amazon S3 and fetching it within the Lambda function execution.
C
Creating a Lambda container image with the library and updating Lambda functions to use this image.
D
Utilizing an Amazon EFS file system to store and mount the library for all Lambda functions.
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