Exam AWS Certified Machine Learning - Specialty topic 1 question 333 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 333
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

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?

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.

Show Suggested Answer

Suggested Answer: A 🗳️

by GS_77 at Sept. 7, 2024, 1:16 p.m.

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Comments

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youonebe

3 months, 2 weeks ago

Selected Answer: A

D is wrong - Unnecessary unless the solution requires modifying the base container (e.g., adding non-scikit-learn dependencies). The problem doesn’t indicate this need.

upvoted 2 times

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VerRi

10 months ago

Selected Answer: A

A. Enough to compare the performance of new and old scikit-learn models. D. Good, but with additional overhead.

upvoted 2 times

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Tkhan1

11 months ago

Selected Answer: D

Amazon SageMaker provides built-in containers for common machine learning frameworks, including scikit-learn, which are designed to handle operational maintenance such as patching, scaling, and monitoring.https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-docker-containers-scikit-learn-spark.html

upvoted 2 times