AIF-C01 practice question: Transparency and explainability (1 of 2)
Guidelines for Responsible AI A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A bank's model risk team wants one governed record for each ML model.
It must hold the intended uses, a risk rating, the training details and the evaluation results, and the team must be able to export the record as a PDF for reviewers.
Which SageMaker AI capability should the team use?
Choose one answer.
AA. SageMaker Model Cards, which document intended uses, risk rating, training details and evaluation results.
BB. SageMaker Model Registry, which stores model versions and their approval status for deployment.
CC. SageMaker Model Monitor, which watches live endpoints for data drift and changes in model quality.
DD. AWS Config, which records configuration changes to AWS resources and evaluates them against rules.
B. Wrong: The registry tracks model versions and approvals. It does not hold intended uses, risk ratings or documented evaluation results.
C. Wrong: Model Monitor watches a deployed model for drift in live data. It does not document a model's intended uses or training details.
D. Wrong: Config records resource configurations, not documentation about an ML model. It cannot hold intended uses or evaluation results.
Explanation
Model cards record what a model is for, its limits, and how it was trained and evaluated, in a structured form that can be exported for governance. The other tools monitor, version or configure models rather than document them. This question tests choosing the documentation tool over the deployment and monitoring tools.
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