MLA-C01 · Question #62
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…
The correct answer is B. The Canvas user must have permissions to access the S3 bucket where the model artifacts are C. The model must be registered in the SageMaker Model Registry. To share an externally developed model with a SageMaker Canvas user, the model must first be registered in the SageMaker Model Registry (C) - this is how Canvas discovers and imports models not built within SageMaker itself. The Canvas user also needs S3 bucket permissions (B)…
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.)
Options
- AThe ML engineer and the Canvas user must be in separate SageMaker domains.
- BThe Canvas user must have permissions to access the S3 bucket where the model artifacts are
- CThe model must be registered in the SageMaker Model Registry.
- DThe ML engineer must host the model on AWS Marketplace.
- EThe ML engineer must deploy the model to a SageMaker endpoint.
How the community answered
(37 responses)- A14% (5)
- B76% (28)
- D3% (1)
- E8% (3)
Explanation
To share an externally developed model with a SageMaker Canvas user, the model must first be registered in the SageMaker Model Registry (C) - this is how Canvas discovers and imports models not built within SageMaker itself. The Canvas user also needs S3 bucket permissions (B) to access the model artifacts, since Canvas retrieves them directly during the import/tuning process.
Why the distractors are wrong:
- A is the opposite of the requirement - both users must be in the same SageMaker domain to share models.
- D (AWS Marketplace) is for publishing models publicly to third parties, not for internal team sharing.
- E (SageMaker endpoint) is for real-time inference, not a prerequisite for sharing/tuning in Canvas.
Memory tip: Think "Register + Read Access" - you register the model so Canvas can find it, and grant S3 access so Canvas can fetch it. Both are needed; neither alone is sufficient.
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