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PMLE Practice Question: Set up end-to-end monitoring for a Vertex AI model
A company wants to set up end-to-end monitoring for a Vertex AI model. Which three components should they include?
⚠ Common exam trap
Many candidates confuse operational or cost-related metrics (like backup status or training cost) with the three core pillars of model monitoring: performance metrics, drift detection, and latency tracking.
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
✓
Model performance metrics
For end-to-end monitoring of a Vertex AI model, the company needs to track how well the deployed model is performing, so B (Model performance metrics) is correct because Vertex AI Model Monitoring surfaces metrics like accuracy, precision, and recall against ground-truth or skewed prediction distributions. C (Data drift and concept drift detection) is correct because Vertex AI Model Monitoring specifically detects training-serving skew and drift in feature distributions (data drift) as well as changes in the relationship between features and labels (concept drift), which are core to maintaining model quality over time. D (Prediction latency) is correct because operational monitoring of an endpoint must include request/response latency to ensure the model meets serving SLOs, and Vertex AI exposes these metrics via Cloud Monitoring. The remaining options do not belong: A (Feature store backup status) concerns data durability of a feature store, not model monitoring, and E (Model training cost) is a billing/training concern rather than an end-to-end runtime monitoring component.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature store backup status
Why it's wrong here
Backup status is infrastructure maintenance.
- ✓
Model performance metrics
Why this is correct
Model performance metrics provide continuous visibility into prediction quality, drift and skew once the Vertex AI model is deployed, satisfying the end-to-end monitoring requirement. Vertex AI Model Monitoring tracks feature and prediction drift against training baselines, alerting when live traffic diverges, so the company detects degradation rather than only infrastructure health.
- ✓
Data drift and concept drift detection
Why this is correct
Concept drift detection tracks shifts in the input-to-output relationship, while data drift monitors changes in feature distributions. Together they satisfy the end-to-end monitoring requirement by catching both covariate shifts and degradation in learned mappings, which latency or resource metrics alone cannot reveal.
- ✓
Prediction latency
Why this is correct
Prediction latency measures the time an endpoint takes to return inferences, directly satisfying the end-to-end monitoring requirement for serving health. It exposes performance regressions, overloaded nodes or inefficient containers that accuracy-focused drift metrics would miss entirely.
- ✗
Model training cost
Why it's wrong here
Training cost is part of cost management, not monitoring.
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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.