PMLE Monitoring ML Solutions Practice Question
An engineer is configuring Vertex AI Model Monitoring for a model deployed on an endpoint. They want to monitor feature skew using the training dataset as a baseline. The training dataset is large (10 TB). What is the most efficient way to provide the baseline distribution?
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
✓
Use the BigQuery table containing the training data as the baseline source in the monitoring configuration
Vertex AI Model Monitoring can automatically compute the baseline distribution from the training dataset stored in BigQuery. This avoids manual computation or exporting large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute the baseline distribution offline and upload a JSON file with feature statistics
Why it's wrong here
This requires manual work and does not leverage BigQuery integration.
- ✗
Upload the entire training dataset as a CSV file to Cloud Storage and reference it in the monitoring config
Why it's wrong here
This is possible but inefficient for large datasets; BigQuery is more scalable.
- ✗
Sample 1% of the training data and use that as baseline
Why it's wrong here
Sampling may not represent the full distribution and could miss skew.
- ✓
Use the BigQuery table containing the training data as the baseline source in the monitoring configuration
Why this is correct
Correct: Vertex AI Model Monitoring supports BigQuery tables as baseline sources.
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Same concept, more angles
1 more way this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An ML engineer has deployed a model on Vertex AI Endpoints and wants to detect when the serving data distribution differs from the training data distribution. Which monitoring feature should they enable?
easy- A.Prediction drift monitoring
- B.Feature drift monitoring
- C.Model quality monitoring
- ✓ D.Feature skew monitoring
Why D: Feature skew monitoring compares the training data distribution (stored in a baseline) with the serving data distribution to detect skew. Feature drift tracks changes over time in serving data only.
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.