MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A team uses SageMaker Clarify to monitor bias drift on a deployed model. They have defined a baseline with training data and set up a monitoring schedule. After one month, they receive a violation report indicating that the post-training metrics have deviated from the baseline. What does this violation indicate?
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
✓
The model's predictions relative to sensitive attributes have shifted compared to the training baseline
SageMaker Clarify bias drift monitoring compares predicted outcomes (post-training) against the baseline to detect changes in fairness metrics like disparate impact. It does not measure prediction accuracy or data quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The model's predictions relative to sensitive attributes have shifted compared to the training baseline
Why this is correct
Clarify's post-training bias metrics compare predicted labels across sensitive attribute groups against the training baseline. A violation means those group-conditional prediction rates have drifted, indicating the model now treats protected groups differently than when trained.
- ✗
The SHAP values for features have changed
Why it's wrong here
Clarify's bias drift monitor compares post-training bias metrics such as disparate impact against baseline thresholds; SHAP values describe feature attributions for explainability, not the monitored bias metric. SHAP analysis is the right tool when you need per-prediction feature attribution reporting rather than drift violation alerts.
- ✗
The model's predictions are becoming less accurate
Why it's wrong here
Clarify's bias drift monitor tracks bias metric deviation, not predictive accuracy; accuracy degradation requires comparing predictions against ground-truth labels via Model Monitor's model quality monitor. Bias monitoring is the right choice when the concern is fairness metrics such as disparate impact diverging from the training baseline.
- ✗
The distribution of input features has changed
Why it's wrong here
Input feature distribution shifts are detected by SageMaker Model Monitor's data quality monitor, which compares captured inference data against a statistics baseline. Clarify's bias monitor evaluates predicted labels and bias metrics instead. Data quality monitoring is correct when the requirement is detecting covariate shift in request payloads.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.