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PMLE Practice Question: You have deployed a regression model that…
You have deployed a regression model that predicts house prices. Over the past month, the model's predictions have been consistently too high. You suspect data drift in the input features. Which monitoring metric should you prioritize to confirm this?
⚠ Common exam trap
Google Cloud often tests the distinction between monitoring prediction drift (output) and feature drift (input), trapping candidates who assume that a change in predictions automatically implies data drift without verifying the input distributions.
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 feature distribution drift using a divergence metric like Jensen-Shannon divergence
The question describes a scenario where predictions are consistently too high, which is a symptom of data drift—a change in the distribution of input features. Monitoring feature distribution drift using a divergence metric like Jensen-Shannon divergence directly measures whether the input data has shifted from the training distribution, which would cause the model to make biased predictions. This is the most direct way to confirm data drift in the input features.
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 prediction drift (prediction distribution)
Why it's wrong here
Prediction drift tracks the model's output distribution, but the stem already establishes that outputs are skewed high; it cannot confirm whether inputs shifted. It is tempting because prediction drift suits unlabelled production data where ground truth is unavailable, yet here you need feature-level distribution comparison against the training baseline.
- ✓
Monitor feature distribution drift using a divergence metric like Jensen-Shannon divergence
Why this is correct
Jensen-Shannon divergence quantifies how far each input feature's live distribution has drifted from its training baseline, directly confirming whether covariate shift explains the biased predictions. Aggregate prediction metrics alone cannot isolate which features moved, so distribution-level comparison is the appropriate diagnostic.
- ✗
Monitor feature attribution drift using SHAP values
Why it's wrong here
SHAP attribution drift measures how much each feature contributes to predictions, not whether the input feature distributions themselves shifted. It tempts because attribution changes often accompany drift, but to confirm input data drift you compare live feature distributions against training baselines, not explanation values.
- ✗
Monitor residual distribution drift
Why it's wrong here
Residual drift reflects prediction error against delayed ground truth, not the input feature distributions the stem suspects. It is tempting because residuals directly capture the consistently high predictions, and residual monitoring would be the right choice once labels arrive to confirm degraded accuracy rather than input drift.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.