MLA-C01 ML Model Development Practice Question
A data scientist wants to use SageMaker Clarify to analyze bias during training of a binary classification model. Which TWO types of bias metrics can SageMaker Clarify compute? (Select TWO.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Post-training bias metrics (e.g., Difference in Positive Proportions, AD)
SageMaker Clarify computes pre-training bias (e.g., class imbalance) and post-training bias (e.g., difference in positive proportions across groups).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature importance
Why it's wrong here
Feature importance is for explainability, not bias.
- ✓
Post-training bias metrics (e.g., Difference in Positive Proportions, AD)
Why this is correct
These metrics are computed on model predictions.
- ✗
SHAP values
Why it's wrong here
SHAP values are for explainability, not bias metrics.
- ✓
Pre-training bias metrics (e.g., Class Imbalance, DPL)
Why this is correct
These metrics are computed on the dataset before training.
- ✗
Confusion matrix
Why it's wrong here
Confusion matrix is a performance metric, not a bias metric.
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