easyMultiple Choice
PDE Practice Question: A company has deployed a classification model on…
A company has deployed a classification model on Vertex AI. They want to detect data drift in real-time for the model's input features. Which service should they use?
⚠ Common exam trap
A common mix-up: candidates confuse general monitoring (Cloud Monitoring) with ML-specific drift detection, assuming any monitoring tool can detect data drift, when in fact Vertex AI Model Monitoring is the only service that performs statistical distribution comparison for model inputs.
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
✓
Vertex AI Model Monitoring
Vertex AI Model Monitoring is the correct service because it is specifically designed to detect data drift and feature skew for models deployed on Vertex AI. It continuously monitors input features against a baseline distribution and alerts when drift exceeds a configured threshold, enabling real-time detection without requiring custom code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Monitoring
Why it's wrong here
Cloud Monitoring tracks infrastructure and application metrics such as CPU, latency and error rates; it does not compute feature distribution statistics against a training baseline. It is the correct tool for alerting on operational health, not for detecting drift in model input features.
- ✗
Cloud Data Loss Prevention
Why it's wrong here
Cloud DLP discovers and classifies sensitive data such as personally identifiable information; it does not compute feature distribution statistics. It would be the right choice for redacting or de-identifying data before training, not for comparing live inference inputs against a training baseline.
- ✗
Cloud Logging
Why it's wrong here
Cloud Logging stores and queries log entries; it cannot compute feature distribution statistics or compare live traffic against a training baseline, which is what drift detection requires. It is tempting because Vertex AI does emit prediction and request logs there, but those logs feed a dedicated drift monitor rather than performing detection themselves.
- ✓
Vertex AI Model Monitoring
Why this is correct
Vertex AI Model Monitoring continuously evaluates incoming prediction requests against the training baseline, computing drift metrics on feature distributions. This satisfies the real-time detection requirement, unlike batch-only tools. It natively integrates with Vertex AI endpoints, so no separate pipeline is needed to surface skew or drift alerts.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.