Topic 1 Question 62
2 つ選択An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning. The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain. Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user?
The ML engineer and the Canvas user must be in separate SageMaker domains.
The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
The model must be registered in the SageMaker Model Registry.
The ML engineer must host the model on AWS Marketplace.
The ML engineer must deploy the model to a SageMaker endpoint.
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コメント(2)
- 正解だと思う選択肢: BC
That's basic how Sagemaker works.
👍 1GiorgioGss2024/11/27 - 正解だと思う選択肢: BC
For model outside of Amazon SageMaker, canvas user needs access to S3; Model --> Model registry
👍 1Saransundar2024/12/04
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