PMLE Monitoring ML Solutions Practice Question
A team is monitoring a model and observes that the error rate (prediction failures) has increased. They have enabled request/response logging on the Vertex AI Endpoint. How can they set up a metric and alert for prediction error rate?
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
✓
Create a log-based metric in Cloud Logging for error logs and set up an alert in Cloud Monitoring
Vertex AI Endpoint logs contain information about failed predictions. You can create a log-based metric in Cloud Logging that counts error logs, and then create an alert in Cloud Monitoring based on that metric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure Cloud Monitoring to pull error rate from Cloud Endpoints
Why it's wrong here
Cloud Endpoints is a separate service for API management, not used for Vertex AI prediction errors.
- ✗
Use Vertex AI Model Monitoring to monitor error rate directly
Why it's wrong here
Model Monitoring does not track error rates; it focuses on data and prediction drift.
- ✓
Create a log-based metric in Cloud Logging for error logs and set up an alert in Cloud Monitoring
Why this is correct
Log-based metrics are the standard way to derive metrics from logs and alert on them.
- ✗
Enable Vertex AI Pipelines to track errors
Why it's wrong here
Pipelines orchestrate workflows, they do not monitor endpoint error rates.
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Written by Johnson Ajibi, MSc IT Security
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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.