AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 58

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? (Choose two.)

Answer options

• A. The ML engineer and the Canvas user must be in separate SageMaker domains.
• B. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
• C. The model must be registered in the SageMaker Model Registry.
• D. The ML engineer must host the model on AWS Marketplace.
• E. The ML engineer must deploy the model to a SageMaker endpoint.

Correct answer: B, C

Explanation

The correct answer is B and C. The Canvas user must have access to the S3 bucket to retrieve the model artifacts, ensuring they can use the model. Additionally, registering the model in the SageMaker Model Registry is necessary for it to be effectively shared and managed within the SageMaker environment. Options A, D, and E are incorrect because they do not meet the requirements for sharing the model in this context.