Exam AWS Certified Developer - Associate DVA-C02 topic 1 question 276 discussion

Quick google will tell you the max size of a lambda layer is 250mb.

https://aws.amazon.com/blogs/compute/using-amazon-efs-for-aws-lambda-in-your-serverless-applications/ Sharing large code packages with Lambda EFS is useful for sharing software packages or binaries that are otherwise too large for Lambda layers. You can copy these to EFS and have Lambda use these packages as if there are installed in the Lambda deployment package.

D is the correct answer.

I've been going back and forward on this one for a few days. I have settled for EFS primarily based off a blog I read from an AWS community builder who specializes in lambda. https://betterdev.blog/serverless-ml-on-aws-lambda/#overcoming_lambda_size_limitations:~:text=the%20DynamoDB%20table.-,Overcoming%20Lambda%20size%20limitations,-If%20we%20package 250mb limit per lambda, although the layers capacity is 75gb this covers your whole environment and breaches the single lambda limit. The blog uses a container solution, the limit here is 10GB which is still to small for our use case. EFS fits this use case even though it is a tad more troublesome to implement. Granted the blog is 2 years old, I'm hoping not much has changed since.

D. Save the library in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system in all the Lambda functions. This approach allows Lambda functions to access large libraries or datasets that exceed the size limits of Lambda's deployment package. By using Amazon EFS, a fully managed elastic file storage, the library can be stored once and mounted onto multiple Lambda functions simultaneously. This eliminates the need to package the library with each Lambda function, which would not be feasible given the size constraints of Lambda layers and deployment packages. Additionally, this method requires minimal code changes, focusing only on configuring the Lambda functions to mount the EFS file system, providing a scalable and efficient solution for making large libraries available to serverless applications.

just the layer limitation 250 mb .

Upto 75 GB can be accommodated. https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html

La solución recomendada para este caso es guardar la biblioteca en capas Lambda y adjuntar esas capas a todas las funciones Lambda. Esto permitirá que todas las funciones Lambda tengan acceso a la biblioteca sin necesidad de duplicarla en cada función. Las capas Lambda son una forma de compartir código y bibliotecas comunes entre varias funciones Lambda. Puedes crear una capa Lambda que contenga la biblioteca de aprendizaje automático y luego adjuntar esa capa a todas las funciones Lambda que necesiten acceder a ella. Al utilizar capas Lambda, puedes reducir el tamaño de las funciones Lambda y simplificar su mantenimiento. Además, si el tamaño de la biblioteca está aumentando, puedes actualizar la capa Lambda sin tener que modificar y volver a implementar todas las funciones Lambda.

S3 takes too long.