MLA-C02 practice question: Bias and class imbalance (1 of 3)
Data Preparation for ML and AI 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 is training a small-business loan approval model. Applications from one age group make up only 9 percent of the training data, and the team has not yet checked whether the dataset itself favors any group.
The bank's ML platform team already runs SageMaker Clarify jobs for its other credit models. The new check must measure dataset-level bias before any model is trained, using the protected attribute in the data, and store the results with the training data for the model-risk team.
Which approach meets these requirements?
Choose one answer.
AA. Run a Clarify job that computes SHAP values on the trained model, and review which features drive each prediction.
BB. Run a SageMaker Clarify processing job that computes pre-training bias metrics, such as class imbalance and difference in proportions of labels, for the age-group facet.
CC. Generate a Glue DataBrew profile of the applications, and review the column distributions for the age-group field with the model-risk team.
DD. Set up a Model Monitor data quality monitor on the endpoint after deployment and review its violations weekly.
Show the answer and explanation
Answer: B
A. Wrong: SHAP values explain a trained model's predictions, which is a different task. The stem asks for dataset-level bias before training.
C. Wrong: A profile shows distributions but does not compute bias metrics for a facet or label proportions. The team needs the bias measures for the model-risk review.
D. Wrong: This runs after the model is deployed and checks incoming data against a baseline, so it cannot check the training dataset before training. It also does not compute the bias metrics.
Explanation
Clarify pre-training bias metrics measure the dataset before training, including class imbalance and label proportions across a protected group, and the results are written to S3 for review. SHAP explains trained predictions, DataBrew profiles without bias metrics, and Model Monitor runs only after deployment.
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