Topic 1 Question 333
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?
Package the code as the training script for an Amazon SageMaker scikit-learn container.
Package the code into a custom-built container. Push the container to Amazon Elastic Container Registry (Amazon ECR).
Package the code into a custom-built container. Push the container to AWS Fargate.
Package the code by extending an Amazon SageMaker scikit-learn container.
ユーザの投票
コメント(5)
- 正解だと思う選択肢: 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
👍 2Tkhan12024/09/18 - 正解だと思う選択肢: A
A. Enough to compare the performance of new and old scikit-learn models. D. Good, but with additional overhead.
👍 2VerRi2024/10/17 - 正解だと思う選択肢: D
D appears to be the best choice
👍 1GS_772024/09/07
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