MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A retail company has a SageMaker model that predicts customer churn. The model was trained on data that included a 'customer_zipcode' feature. After deployment, the data science team notices that the model's predictions for certain zip codes have become less accurate over time. They suspect that the relationship between zip code and churn has changed due to a recent relocation of a major employer. Which SageMaker monitoring capability should they use to detect this type of drift?
⚠ Common exam trap
The trap here is assuming that any change in feature distribution or importance is equivalent to concept drift, when actually concept drift is a change in the underlying relationship that degrades model performance.
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 Model Monitor model quality monitoring
Model quality monitoring is designed to monitor the performance of a model by comparing predictions to actual labels. When the relationship between a feature and the target changes, the model's accuracy drops, and model quality monitoring will detect this drift. Data quality monitoring only looks at input distributions, bias drift focuses on fairness, and feature attribution drift looks at feature importance, none of which directly measure predictive performance.
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 bias drift monitoring
Why it's wrong here
Bias drift monitoring tracks changes in bias metrics such as disparate impact across groups. While zip code could be a sensitive attribute, the scenario describes a change in the feature-target relationship affecting overall accuracy, not a fairness issue. Bias drift would not necessarily detect the overall performance degradation.
- ✓
SageMaker Model Monitor model quality monitoring
Why this is correct
Model quality monitoring evaluates the model's predictive performance against ground truth labels over time. It can detect concept drift by measuring metrics like accuracy or AUC and alerting when they degrade. Since the relationship between zip code and churn has changed, the model's predictions become less accurate, which model quality monitoring will catch.
- ✗
SageMaker Model Monitor feature attribution drift monitoring
Why it's wrong here
Feature attribution drift monitoring compares the relative importance of features (e.g., SHAP values) between baseline and current data. It could show that zip code's importance changed, but it does not directly measure prediction accuracy. The team needs to detect degraded performance, which is best done with model quality monitoring.
- ✗
SageMaker Model Monitor data quality monitoring
Why it's wrong here
Data quality monitoring detects changes in the distribution of input features, such as a shift in the frequency of zip codes. However, it does not detect changes in the relationship between features and the target variable. The issue described is concept drift, where the mapping from zip code to churn has changed, not just the input distribution.
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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
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.