MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A financial services company is deploying a fraud detection model on SageMaker. To comply with regulations, they must ensure that the model's predictions are not biased against protected groups. They plan to monitor bias drift post-deployment using SageMaker Clarify. Which data inputs are required to configure Clarify's bias drift monitoring?
⚠ Common exam trap
Many exam-takers assume only inference data is needed for monitoring, overlooking the critical requirement of a baseline training dataset with ground truth labels to measure drift against.
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
✓
Baseline training data with ground truth labels and inference data with predictions
SageMaker Clarify's bias drift monitoring requires a baseline—specifically, the training data with ground truth labels—to establish the original bias metrics, and the inference data with predictions to compute post-deployment bias metrics. By comparing these two datasets, Clarify detects statistically significant shifts in bias over time, which is essential for regulatory compliance in fraud detection models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Only the inference data with predictions
Why it's wrong here
Bias drift monitoring compares live inference data against a baseline dataset with known labels, so predictions alone cannot reveal drift. Clarify needs both the baseline training data and the captured inference data with predicted labels. Supplying only inference data suits endpoint-level data capture, not bias drift analysis.
- ✗
Only the ground truth labels for recent predictions
Why it's wrong here
Bias drift monitoring compares prediction distributions against a baseline, so it needs the input features and predictions, not just labels. Ground truth alone is insufficient because protected-group attributes live in the feature data. Labels are required for model quality monitoring, a different monitor.
- ✗
Only the training data with feature attributions
Why it's wrong here
Training data with attributions describes the original fitted model, not live traffic. Bias drift monitoring requires the captured endpoint data and a baseline, since drift is measured between recent predictions and the baseline distribution. Attributions belong to explainability monitoring instead.
- ✓
Baseline training data with ground truth labels and inference data with predictions
Why this is correct
Clarify's bias drift monitoring compares the baseline distribution against live inference data, so it needs labelled baseline training data (ground truth) plus inference data containing predictions. Without both, it cannot compute bias metrics or detect drift across protected groups.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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