AWS Certified Machine Learning Engineer - Associate MLA-C01 — Topic 1 Question 62

Question

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.)

Accepted Answer

Correct answer: B, C. The correct answer is B, C. Option B: The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored. Option C: The model must be registered in the SageMaker Model Registry. Amazon S3 provides durable, scalable object storage that is fully managed. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

AWS Certified Machine Learning Engineer - Associate MLA-C01 — Question 62