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PMLE Practice Question: Your team has a production ML model on Vertex AI…
Your team has a production ML model on Vertex AI that shows a gradual decline in accuracy over the past week. The model is retrained weekly using the latest data. Which monitoring approach should you implement to detect the issue earlier?
⚠ Common exam trap
PMLE often tests the distinction between infrastructure monitoring (uptime, latency, request count) and model-specific monitoring (data drift, skew, accuracy), so candidates must recognize that only Vertex AI Model Monitoring addresses data distribution changes that precede accuracy drops.
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
✓
Configure Vertex AI Model Monitoring to detect feature drift and alert when metrics exceed thresholds.
Vertex AI Model Monitoring is designed to detect data drift and concept drift by comparing the distribution of incoming prediction requests against a baseline (e.g., training data). A gradual decline in accuracy over a week strongly suggests feature drift—the input data distribution has shifted away from what the model was trained on. By configuring drift thresholds and alerts, you can catch the drift before it significantly degrades model performance, enabling earlier retraining or investigation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure Vertex AI Model Monitoring to detect feature drift and alert when metrics exceed thresholds.
Why this is correct
Vertex AI Model Monitoring computes feature drift against a training baseline and raises alerts when thresholds are breached, catching distributional shifts before weekly retraining would. This detects the gradual accuracy decline earlier than waiting for the next scheduled retrain.
- ✗
Create a Cloud Monitoring alert for prediction response count.
Why it's wrong here
Response count measures traffic volume, not whether predictions remain accurate, so drift would go unnoticed. It is tempting because request-rate alerts are the usual first monitoring step, and would be correct if the concern were sudden traffic drops or quota exhaustion rather than accuracy degradation.
- ✗
Use BigQuery ML to retrain the model more frequently.
Why it's wrong here
Retraining more frequently addresses model drift only after labelled outcomes arrive, so it cannot surface the accuracy decline earlier; the stem asks for monitoring. BigQuery ML suits building or retraining models in-warehouse, but detecting degradation requires Vertex AI Model Monitoring with drift or skew thresholds on serving traffic.
- ✗
Set up a Cloud Monitoring uptime check on the prediction endpoint.
Why it's wrong here
An uptime check only verifies that the endpoint responds; it cannot detect accuracy drift, which is a data and prediction-quality problem. It is tempting because uptime checks are the standard tool for availability incidents, and would be correct if the symptom were endpoint outages rather than gradual accuracy loss.
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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 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.