A model operations team needs to include a machine learning model trained and deployed entirely in Amazon SageMaker in their watsonx.governance AI inventory. What is the required first step?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Think of it this way: in real-world AI governance, register the external sagemaker model in watsonx.governance to associate it with a use case and enable ongoing metric capture into factsheets is exactly what teams reach for when they need to handle this scenario. watsonx. On the exam, remember that this falls squarely under the 2.0 AI Lifecycle Governance domain.
Full explanation below image
Full Explanation
watsonx.governance supports external model governance by allowing models built and deployed outside of the IBM ecosystem — including Amazon SageMaker, Google Vertex AI, and Microsoft Azure ML — to be registered in the platform. Registration associates the external model with a governance use case, enabling the platform to capture available metadata and metrics into the AI Factsheet. This capability is essential for organizations with multi-cloud or hybrid AI environments who need centralized governance visibility across all models regardless of where they were built or deployed. The correct answer, "Register the external SageMaker model in watsonx.governance to associate it with a use case and enable ongoing metric capture into Factsheets", directly addresses the scenario described because it aligns with the specific governance requirement in question. The incorrect options ("Re-train the SageMaker model inside Watson Studio to enable automatic Factsheet capture from the IBM native runtime", "Export SageMaker model metrics manually to a CSV file and upload them into watsonx.governance as a one-time snapshot", "Deploy an IBM monitoring agent inside the AWS account to automatically mirror all SageMaker model metrics in real time") may seem plausible but do not satisfy the core requirement. Understanding the distinction between these concepts is critical for IBM watsonx.governance implementations and is frequently tested in the 2.0 AI Lifecycle Governance section of the certification exam.