PMLE Monitoring ML Solutions Practice Question
A company wants to be alerted when the prediction error rate on their Vertex AI Endpoint exceeds 5% in any 5-minute window. What is the best way to set up this alert?
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 Cloud Monitoring alert on the metric 'prediction/error_count' with a threshold of 5% of total predictions
Cloud Monitoring can create an alerting policy based on the 'Prediction error count' metric with a rolling 5-minute window and a threshold of 5% (or absolute count).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a Cloud Monitoring alert on the metric 'prediction/error_count' with a threshold of 5% of total predictions
Why this is correct
Correct: Cloud Monitoring supports alerting on error rate metrics.
- ✗
Set up a scheduled Cloud Function to query logs and check error rate
Why it's wrong here
This is less efficient and not near-real-time compared to Cloud Monitoring alerts.
- ✗
Enable BigQuery log sink and create a scheduled query alert
Why it's wrong here
Scheduled queries are for batch analysis, not real-time alerting.
- ✗
Use Vertex AI Model Monitoring to detect prediction drift
Why it's wrong here
Prediction drift monitors changes in prediction distribution, not error rate.
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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.