AWS Certified Machine Learning – Specialty — Question 362

An agriculture company wants to improve crop yield forecasting for the upcoming season by using crop yields from the last three seasons. The company wants to compare the performance of its new scikit-learn model to the benchmark.

A data scientist needs to package the code into a container that computes both the new model forecast and the benchmark. The data scientist wants AWS to be responsible for the operational maintenance of the container.

Which solution will meet these requirements?

Answer options

• A. Package the code as the training script for an Amazon SageMaker scikit-learn container.
• B. Package the code into a custom-built container. Push the container to Amazon Elastic Container Registry (Amazon ECR).
• C. Package the code into a custom-built container. Push the container to AWS Fargate.
• D. Package the code by extending an Amazon SageMaker scikit-learn container.

Correct answer: A

Explanation

Using a prebuilt Amazon SageMaker container with a custom training script allows AWS to handle the operational maintenance, patching, and updates of the container environment. Creating a custom container or extending an existing one increases the operational burden on the data scientist to manage the base image and dependencies. Therefore, leveraging the managed SageMaker scikit-learn container directly is the most operationally efficient solution.