PMLE Collaborating to manage data and models Practice Question
A company stores training data in Cloud Storage and uses Vertex AI Training for model training. They want to implement a data validation pipeline to detect data drift before retraining. Which service should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between a general-purpose data processing tool (Dataflow) and a specialized managed service (Vertex AI Model Monitoring), leading candidates to choose Dataflow because they think they need to build a custom pipeline, while the question asks for the service that should be used, implying the most appropriate managed solution.
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 designed specifically to detect data drift and feature skew in production ML models by continuously comparing prediction requests against a baseline training dataset. It provides automated alerts when statistical distributions shift beyond a defined threshold, making it the correct choice for a data validation pipeline before retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vertex AI Model Monitoring
Why this is correct
Vertex AI Model Monitoring can detect data drift by comparing distributions.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML is for building models, not drift detection.
- ✗
Cloud Data Loss Prevention
Why it's wrong here
DLP is for data privacy, not drift detection.
- ✗
Dataflow
Why it's wrong here
Dataflow is a processing engine, not a drift detection service.
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JA
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.