MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A team wants to use SageMaker Clarify to monitor bias in their production model predictions. They have configured a bias drift monitor. What does SageMaker Clarify compare to detect bias drift?
⚠ Common exam trap
MLA-C01 often tests the difference between data drift, bias drift, and feature attribution drift; candidates may confuse bias drift with data drift.
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
✓
Current bias metrics against a baseline bias metrics computed from training data
SageMaker Clarify bias drift monitoring compares the current bias metrics (e.g., disparate impact) computed on live data against a baseline bias metric computed from the training data. This detects if bias has drifted over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Current input data distribution against the training data distribution
Why it's wrong here
Clarify's bias drift monitor compares current input data against the baseline established from the training dataset, not the training data distribution itself. Comparing against training distribution is tempting because Clarify's pre-training bias metrics do use training data, but drift detection requires a monitored baseline computed from the training set's statistics.
- ✓
Current bias metrics against a baseline bias metrics computed from training data
Why this is correct
SageMaker Clarify's bias drift monitor compares bias metrics computed on current production data against baseline bias metrics derived from the training dataset. This satisfies the stem's requirement to detect drift by quantifying divergence from the original training distribution, flagging when live predictions deviate from the model's established fairness baseline.
- ✗
Current SHAP feature attributions against baseline SHAP values
Why it's wrong here
Bias drift monitors the distribution of input features and predictions, not SHAP attributions; SHAP values explain individual predictions and are compared for explainability drift, a separate monitor type. It is tempting because Clarify computes SHAP values, so attributions appear central to its monitoring capability.
- ✗
Current predictions against ground truth labels collected in real-time
Why it's wrong here
Bias drift compares the live input data distribution against the baseline captured during training, not predictions against freshly gathered labels; real-time ground truth is rarely available and is used for model quality monitoring instead. It is tempting because label-based comparison underpins accuracy drift detection.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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