AWS Certified Developer – Associate (DVA-C02) — Question 294

A developer needs to implement a custom machine learning (ML) library in an application. The size of the library is 15 GB. The size of the library is increasing. The application uses AWS Lambda functions. All the Lambda functions must have access to the library.

Which solution will meet these requirements?

Answer options

• A. Save the library in Lambda layers. Attach the layers to all Lambda functions.
• B. Save the library in Amazon S3. Download the library from Amazon S3 inside the Lambda function.
• C. Save the library as a Lambda container image. Redeploy the Lambda functions with the new image.
• D. Save the library in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system in all the Lambda functions.

Correct answer: D

Explanation

Amazon EFS is the ideal solution because it allows mounting a shared, elastic file system to multiple AWS Lambda functions, easily accommodating the 15 GB library size and its future growth. Lambda layers have a strict unzipped size limit of 250 MB, and Lambda container images have a maximum size limit of 10 GB, making both options A and C mathematically impossible. Downloading a 15 GB file from Amazon S3 (Option B) on each invocation would introduce massive latency and likely exceed the maximum 15-minute execution timeout of Lambda.