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MLA-C01 Practice Question: A team uses SageMaker Clarify to monitor bias…
A team uses SageMaker Clarify to monitor bias drift in production. They schedule weekly analysis. After a month, Clarify reports a significant increase in a bias metric. What should the team do first?
⚠ Common exam trap
MLA-C01 often tests the impulse to immediately retrain or disable monitoring — the correct first step is always to investigate the report to understand the root cause before acting.
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
✓
Review the analysis report to understand which feature and segment contributed to the drift.
When SageMaker Clarify reports a significant increase in a bias metric, the first step is to review the analysis report to identify which feature and segment drove the drift. This diagnostic step informs whether the drift is due to data distribution shift, a specific subgroup, or a false positive. Only after understanding the cause should the team consider retraining or adjusting monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable the bias monitor because the metric may be noisy.
Why it's wrong here
Disabling the monitor discards the very signal it exists to surface; a rising bias metric indicates the model's predictions have drifted relative to a protected group, which warrants investigation, not suppression. Tempting when teams distrust metric variance, but disabling is only defensible after confirming the monitor itself is misconfigured.
- ✗
Immediately retrain the model with a balanced dataset.
Why it's wrong here
Retraining immediately skips diagnosis: the drift may stem from a shifted input distribution, a broken feature pipeline, or a labelling change, and a balanced dataset would not address those. Retraining with rebalanced data is the right remedy once root-cause analysis confirms class imbalance is the driver.
- ✗
Increase the frequency of analysis to daily.
Why it's wrong here
Daily analysis detects drift sooner but does nothing about the bias already reported; the team still needs to investigate the cause. Increasing frequency is appropriate as a follow-up hardening step once the current breach is understood, not as the first response to an active alert.
- ✓
Review the analysis report to understand which feature and segment contributed to the drift.
Why this is correct
The report identifies the specific feature and segment driving the metric shift, which is needed before choosing any remediation. Acting on the metric value alone risks misdirected fixes. Reviewing the contributing attributes first establishes the cause the team must address.
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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
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.