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PMLE Practice Question: Monitoring a classification model that predicts…
You are monitoring a classification model that predicts loan default. The model was trained on data from 2020-2022. In 2023, the economic conditions changed, and the model's accuracy dropped significantly. Which monitoring approach would best help you detect this issue early?
⚠ Common exam trap
Many exam-takers choose monitoring feature drift (Option B) because it sounds technical, but they overlook that concept drift—a change in the relationship between features and the target—is better detected by monitoring prediction distribution shifts, not just feature distribution shifts.
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
✓
Monitor the prediction distribution for significant shift from training distribution
Monitoring the prediction distribution for a significant shift from the training distribution directly detects changes in the model's output behavior, which is the earliest indicator of concept drift or data drift caused by economic changes. Unlike accuracy monitoring, this approach does not require labeled data, enabling real-time detection of performance degradation before ground truth labels become available.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Monitor the accuracy of the model on the latest batch of labeled data
Why it's wrong here
Labels are often delayed; early detection is not possible.
- ✗
Monitor feature distribution drift using KS test
Why it's wrong here
Feature drift may not capture changes in the relationship between features and target.
- ✓
Monitor the prediction distribution for significant shift from training distribution
Why this is correct
Prediction distribution shift can indicate concept drift even without labels.
- ✗
Monitor the freshness of the training data
Why it's wrong here
Freshness alone does not indicate concept drift.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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