easyMultiple Choice
PMLE Practice Question: A team just moved a model from prototype to…
A team just moved a model from prototype to production using Vertex AI. They notice prediction errors for certain inputs that were not present in training data. What should they do to detect such issues automatically?
⚠ Common exam trap
Google Cloud often tests the distinction between monitoring for operational errors (e.g., HTTP errors) versus monitoring for model-specific issues (e.g., data drift), leading candidates to choose Cloud Logging (Option C) when the correct answer requires a dedicated ML monitoring service.
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 Vertex AI Model Monitoring to detect prediction anomalies
Vertex AI Model Monitoring is specifically designed to detect prediction anomalies, such as data drift and feature skew, by comparing production prediction requests against the training data distribution. This allows the team to automatically identify inputs that deviate from the training data, even if those exact inputs were not present during training, without manual inspection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set up Vertex AI Experiments to compare predictions
Why it's wrong here
Vertex AI Experiments tracks training runs, parameters and metrics for comparison, not production input distributions. It tempts because it is a Vertex AI observability feature, but it operates on experiment metadata rather than live prediction requests, so it cannot flag inputs absent from training data.
- ✗
Use BigQuery ML to analyze prediction requests
Why it's wrong here
BigQuery ML trains and serves models; it does not monitor live Vertex AI prediction traffic for input drift. It tempts because BigQuery can store and query logged requests, but detection requires Vertex AI Model Monitoring, which compares serving inputs against the training baseline automatically.
- ✗
Enable Cloud Logging and set up alerts for error logs
Why it's wrong here
Cloud Logging alerts fire on error entries, but out-of-distribution inputs often return successful predictions with no error logged. It tempts because logging is a familiar monitoring route, yet detecting unseen inputs requires Vertex AI Model Monitoring's training-serving skew and drift analysis.
- ✓
Enable Vertex AI Model Monitoring to detect prediction anomalies
Why this is correct
Vertex AI Model Monitoring compares incoming prediction requests against the training baseline and flags anomalies such as feature skew or out-of-range inputs. This automatically surfaces the unseen inputs that cause prediction errors, without manual inspection.
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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.