easyMultiple Choice
PMLE Practice Question: A retail company has deployed a machine learning…
A retail company has deployed a machine learning model using Vertex AI Endpoints to predict inventory demand. The model was trained on data from the past two years and has been in production for six months. The team has enabled Vertex AI Model Monitoring to track prediction drift with an alert threshold of 0.2. Last week, they received an alert that the prediction drift score reached 0.35, exceeding the threshold. The engineer checks the monitoring dashboard and sees that the distribution of predictions has shifted noticeably compared to the training data. The engineer also notices that the model's accuracy metrics, computed from weekly ground truth data, have remained within acceptable range. What should the engineer do first?
⚠ Common exam trap
The trap here is conflating prediction drift with model degradation and jumping to remediation (retrain, rollback, or raise threshold) instead of first diagnosing whether input data drift is the root cause.
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
✓
Investigate the input feature distributions for the recent serving requests to identify if data drift is the underlying cause of the prediction drift.
Prediction drift is a downstream symptom, and the first diagnostic step is to determine whether it stems from input data drift, concept drift, or a benign shift in the input mix. Since accuracy on weekly ground truth is still acceptable, the model itself is not yet degraded, so the engineer should investigate the recent serving feature distributions against the training baseline before taking any corrective action. Vertex AI Model Monitoring can surface feature-level drift metrics that pinpoint which inputs moved.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Investigate the input feature distributions for the recent serving requests to identify if data drift is the underlying cause of the prediction drift.
Why this is correct
Prediction drift with stable accuracy points to changed inputs rather than model degradation, so examining recent serving feature distributions confirms whether data drift underlies the shift. This satisfies the stem's need to identify the root cause before retraining or altering thresholds.
- ✗
Increase the prediction drift alert threshold to 0.4 to reduce the number of false alerts.
Why it's wrong here
Raising the threshold suppresses the alert without addressing the underlying distribution shift, hiding a genuine signal. It tempts because threshold tuning reduces alert noise, but that is valid only after confirming the drift is benign; here the score exceeds the configured limit and needs investigation.
- ✗
Retrain the model using the latest three months of data to incorporate recent trends.
Why it's wrong here
Retraining immediately acts on drift before diagnosing its cause, and prediction-distribution shift with stable accuracy may reflect legitimate demand change rather than degradation. It tempts because retraining is the usual remedy for stale models, but investigation should precede any retraining or rollback decision.
- ✗
Roll back to an earlier model version that had lower prediction drift.
Why it's wrong here
Rolling back discards the current model despite ground-truth accuracy remaining acceptable, and the earlier version may drift equally under the new data. It tempts because rollback is the standard response to a bad deployment, but here prediction drift alone, without accuracy loss, does not justify reverting.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PMLE question from scratch — 775 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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 Google Cloud exam blueprint
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.