ExamPark
Course menuMLA-C02

Exams / MLA-C02 / Bias and class imbalance / Question 3

MLA-C02 practice question: Bias and class imbalance (3 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.

Last verified against current AWS documentation ·How we write our questions

A regional bank has deployed a credit-decision model. Applicants from one protected group are approved far less often than others, and the model-risk team wants to measure the bias in the model's predictions rather than in the training labels.

The team already uses SageMaker Clarify for its pre-training analysis. The new check must compare outcomes across groups using the model's predicted labels, on the same bias facet as that analysis.

Which metric should the team use?

Choose one answer.

  1. A. Class imbalance, computed on the training labels before the model was trained, because it measures how often each age group appears in the dataset.
  2. B. SHAP feature importance for the protected attribute, because a large importance value shows the model is biased.
  3. C. Accuracy of the model computed on the test set, broken down only by overall error rate and not by group.
  4. D. Difference in positive proportions in predicted labels, computed by Clarify as a post-training bias metric on the model's predictions.
Show the answer and explanation

Answer: D

  • A. Wrong: Class imbalance is a pre-training measure of how often each group appears in the dataset. It does not use the model's predictions, so it cannot show how the model treats each group.
  • B. Wrong: SHAP shows how features contribute to predictions, which is useful for explanations. It is not a bias metric comparing outcome rates across groups.
  • C. Wrong: Overall accuracy hides the group-level outcome gap the team needs to measure. The stem asks for a comparison across the protected groups.
  • D. Right: Correct. This post-training metric compares the rate of positive predictions between groups, which is the outcome the team needs to measure. Clarify computes it from predictions on the bank's chosen facet. Learn more: Post-training data and model bias metrics (opens in a new tab)

Explanation

Post-training bias metrics use the model's predictions. Difference in positive proportions in predicted labels compares positive outcome rates across groups, which exposes the approval gap. Pre-training metrics describe the data, SHAP explains predictions, and overall accuracy hides group differences.

Study

Keep practicing

Answer questions with a score at the end, or practice just this topic with the explanation after each one. No sign-up needed.

Take the free MLA-C02 practice examPractice Bias and class imbalanceAll Bias and class imbalance questions

Want more on Bias and class imbalance? The 5 full-length practice sets have 5 more Bias and class imbalance questions, 325 in all, none of them in the free exam. One payment of $14.99, no subscription. See the practice sets