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MLA-C01 Practice Question: A financial services company is deploying a model…
A financial services company is deploying a model for loan approval. They must ensure that the model's predictions do not show bias against protected groups. They plan to monitor for bias drift after deployment. Which SageMaker feature should they use?
⚠ Common exam trap
AWS often tests the distinction between data quality monitoring (which tracks input data drift) and bias drift monitoring (which tracks fairness in predictions), leading candidates to mistakenly choose SageMaker Model Monitor when the question specifically asks about bias.
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
✓
SageMaker Clarify with bias drift detection.
SageMaker Clarify is the correct choice because it provides built-in bias detection and monitoring capabilities, including the ability to detect bias drift over time after deployment. It can analyze predictions for protected groups and generate reports on metrics like disparate impact and conditional demographic disparity, which directly addresses the requirement to monitor for bias drift post-deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Model Monitor with data quality monitoring.
Why it's wrong here
Data quality monitoring tracks feature distributions and missing values, not the fairness metrics needed to detect bias drift against protected groups. It is tempting because Model Monitor is the drift-detection service, and would be correct for schema or distribution drift, but bias drift requires SageMaker Clarify bias monitoring.
- ✗
SageMaker Debugger to capture tensors.
Why it's wrong here
Debugger captures tensors, gradients and hardware metrics during training to diagnose convergence issues; it holds no bias metrics or drift baselines. SageMaker Clarify computes pre-training and post-training bias metrics and monitors bias drift against a configured baseline, which is what this scenario requires.
- ✗
SageMaker Ground Truth for fairness labels.
Why it's wrong here
Ground Truth produces human-labelled datasets for training and evaluation, not ongoing bias detection; it cannot compute bias metrics or monitor drift on a live endpoint. It is tempting because fairness labels underpin bias measurement, and Ground Truth would be correct when building a labelled fairness evaluation set before deployment.
- ✓
SageMaker Clarify with bias drift detection.
Why this is correct
SageMaker Clarify computes bias metrics against protected attributes and, with bias drift detection, continuously compares live inference data to the baseline, alerting when disparity shifts. This directly satisfies the requirement to monitor bias drift post-deployment.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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