MLA-C02 practice question: Explaining model outputs
ML Model and Foundation Model (FM) Development A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.
A regional bank uses a gradient-boosted model to decide small-business loan applications, and it already runs SageMaker Clarify processing jobs for its bias reports. Examiners require the bank to explain, for each declined application, which features most influenced that decision, using one method across the portfolio.
The explanations must be produced from the trained model and the bank's historical applications in S3, and they must be reviewable as reports, not ad hoc notebook output.
Which approach meets these requirements?
Choose one answer.
AA. Use a Clarify processing job with SHAP explainability on the trained model and the application data, and store the attribution report in S3.
BB. Read the model's feature importance values from a notebook after training, and copy the top features into the examiner memo by hand.
CC. Run a Clarify job that computes the pre-training bias metrics on the application data, and use those metrics as the explanation for each declined applicant.
DD. Turn off the model's feature inputs and use only the loan amount, so the decisions become simple enough to explain without any tool.
Show the answer and explanation
Answer: A
A. Right: Correct. Clarify computes SHAP-based feature attributions for the model and dataset in a managed processing job, and writes a report to S3 that reviewers can use. This gives per-decision explanations at portfolio scale. Learn more: Model explainability with SageMaker Clarify (opens in a new tab)
B. Wrong: Manual copying does not produce a repeatable, portfolio-wide report, and the stem asks for reports rather than notebook output. The method also would not show per-decision attributions consistently.
C. Wrong: Pre-training bias metrics describe the dataset, not the attributions for each decision. They do not explain which features influenced a particular decline.
D. Wrong: Removing features changes the model and may reduce its accuracy, and it does not produce explanations for the existing decisions. The examiners asked for explanations of the current approach.
Explanation
Clarify computes SHAP-based feature attributions from the trained model and data, and writes a report to S3, which gives reviewable explanations across the portfolio. Manual notes, pre-training metrics and removing features each fail to explain the existing decisions in a repeatable way.
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