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
Without baseline data, Clarify cannot compute relative bias drift.
- ✗
Only the ground truth labels for recent predictions
Why it's wrong here
Ground truth alone is insufficient; baseline distributions are needed.
- ✗
Only the training data with feature attributions
Why it's wrong here
Bias monitoring requires both training and inference data for drift comparison.
- ✓
Baseline training data with ground truth labels and inference data with predictions
Why this is correct
Clarify bias monitoring requires a baseline dataset (training data with labels) and current inference data (with predictions and ground truth when available) to compute bias metrics over time.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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