PMLE Monitoring ML Solutions Practice Question
A retail company has deployed a demand forecasting model on a Vertex AI Endpoint. The model uses 20 numeric features. The MLOps team wants Vertex AI Model Monitoring to detect training-serving skew for each feature and receive alerts when skew exceeds a threshold. They have enabled Model Monitoring for the endpoint and configured a monitoring frequency of every 24 hours. However, after several days, no skew metrics appear in the Vertex AI console. What is the most likely cause?
⚠ Common exam trap
The trap here is assuming that enabling Model Monitoring automatically creates a baseline from the deployed model's training data without explicitly providing it.
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
✓
The training dataset used for the baseline was not provided or the baseline statistics were not generated.
Training-serving skew requires a baseline distribution from the training data. Without providing the training dataset or generating baseline statistics during monitoring configuration, Vertex AI cannot compute skew. The monitoring frequency, request volume, and prediction logging are not the cause. The team must supply the training data or enable baseline generation to see skew metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The monitoring frequency must be set to every 1 hour for skew detection to work.
Why it's wrong here
Vertex AI Model Monitoring supports daily monitoring; hourly frequency is not required for skew detection. The issue is not the frequency but the absence of a training baseline. Without baseline statistics, skew cannot be computed. Setting frequency to 1 hour would still yield no skew metrics because the baseline is missing. This option misdiagnoses the root cause.
- ✗
The endpoint must have at least 1000 prediction requests per day for skew metrics to appear.
Why it's wrong here
There is no minimum daily request threshold for skew metrics to be computed. Vertex AI Model Monitoring can compute skew with fewer requests, though statistical significance may vary. The absence of metrics is not due to low traffic but due to missing baseline configuration. This option invents a requirement that does not exist.
- ✗
The model's predictions must be logged to BigQuery before skew can be detected.
Why it's wrong here
While logging predictions to BigQuery can be part of monitoring, it is not a prerequisite for skew detection. Skew detection uses the input features of prediction requests, not the logged predictions. The real issue is the missing training baseline. This option confuses prediction logging with skew computation.
- ✓
The training dataset used for the baseline was not provided or the baseline statistics were not generated.
Why this is correct
Training-serving skew compares live prediction feature distributions against the training data distribution. If no baseline dataset was supplied when configuring the monitoring job, or if baseline statistics were not generated, Vertex AI cannot calculate skew. The console will show no skew metrics. Providing the training data or enabling baseline generation is required for skew detection.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.