Learn how to efficiently access your company’s internal data science libraries and reference data from AWS Lambda functions connected to a central VPC. Discover the best solution for independent updates and uploads by separate teams.
Table of Contents
Question
A developer has AWS Lambda functions that need to access a company’s internal data science libraries and reference data. Separate teams manage the libraries and the data. The teams must be able to update and upload new data independently. The Lambda functions are connected to the company’s central VPC.
Which solution will provide the Lambda functions with access to the libraries and data?
A. Attach an Amazon Elastic Block Store (Amazon EBS) volume to the Lambda functions by using EBS Multi-Attach in the central VPC. Update the Lambda function execution roles to give the functions to access the EBS volume. Update the Lambda function code to reference the files in the EBS volume.
B. Compress the libraries and reference data in a Lambda /tmp folder. Update the Lambda function code to reference the files in the /tmp folder.
C. Set up an Amazon Elastic File System (Amazon EFS) file system with mount targets in the central VPConfigure the Lambda functions to mount the EFS file system. Update the Lambda function execution roles to give the functions to access the EFS file system.
D. Set up an Amazon FSx for Windows File Server file system with mount targets in the central VPC. Configure the Lambda functions to mount the Amazon FSx file system. Update the Lambda function execution roles to give the functions to access the Amazon FSx file system.
Answer
The correct solution is to set up an Amazon Elastic File System (Amazon EFS) file system with mount targets in the central VPC, configure the Lambda functions to mount the EFS file system, and update the Lambda function execution roles to give the functions access to the EFS file system (Option C).
Explanation
- Amazon EFS provides a scalable, fully managed, and shared file storage system that can be accessed concurrently by multiple Lambda functions and other AWS services. This allows separate teams to update and upload new libraries and data independently.
- By creating mount targets for the EFS file system in the central VPC, the Lambda functions connected to the VPC can easily access the files stored in EFS.
- Configuring the Lambda functions to mount the EFS file system ensures that the functions can read and write files to the shared storage.
- Updating the Lambda function execution roles grants the necessary permissions for the functions to access the EFS file system securely.
The other options are not suitable for the given scenario:
- Option A: Amazon EBS volumes are not the best fit for this use case because they are not designed for concurrent access by multiple Lambda functions and cannot be easily shared among separate teams.
- Option B: Compressing the libraries and reference data in the Lambda /tmp folder is not a scalable solution and does not allow for independent updates by separate teams.
- Option D: Amazon FSx for Windows File Server is designed for Windows-based applications and is not the optimal choice for Lambda functions running in a Linux environment.
In summary, setting up an Amazon EFS file system with mount targets in the central VPC, configuring the Lambda functions to mount the file system, and updating the execution roles is the most suitable solution for providing the Lambda functions with access to the company’s internal data science libraries and reference data while allowing separate teams to manage the resources independently.
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