PMLE Monitoring ML Solutions Practice Question
An MLOps engineer is setting up monitoring for a deployed model on Vertex AI Endpoints. Which TWO actions are required to enable Vertex AI Model Monitoring for feature skew and drift? (Choose two.)
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
✓
Enable request/response logging on the Vertex AI Endpoint
To enable model monitoring, you must enable request/response logging on the endpoint (to capture serving data) and create a monitoring job with the desired configuration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export ground truth labels to Cloud Storage
Why it's wrong here
Ground truth is needed for quality monitoring, not for skew/drift detection.
- ✓
Enable request/response logging on the Vertex AI Endpoint
Why this is correct
Logging captures the serving data needed for monitoring.
- ✗
Enable Vertex AI Pipelines to run scheduled monitoring
Why it's wrong here
Pipelines are not required for monitoring; ModelMonitoringJob runs independently.
- ✓
Create a ModelMonitoringJob with a monitoring configuration
Why this is correct
The monitoring job defines what to monitor and how.
- ✗
Deploy the model with an explanation spec
Why it's wrong here
Explanation spec is for explainability, not for basic drift 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.