PMLE Monitoring ML Solutions Practice Question
An ML engineer wants to monitor the performance of a Vertex AI Endpoint. Which TWO metrics are available in Cloud Monitoring for Vertex AI Endpoints? (Choose 2)
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
✓
Error count
Cloud Monitoring for Vertex AI Endpoints includes metrics like prediction latency (p50, p95, p99) and error count/rate. CPU/GPU utilization is also available for endpoint machines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model accuracy
Why it's wrong here
Model accuracy requires ground truth and is not a built-in Cloud Monitoring metric.
- ✓
Error count
Why this is correct
Correct: Error count is available as a metric.
- ✗
Feature skew score
Why it's wrong here
Feature skew scores are not Cloud Monitoring metrics; they are part of Model Monitoring.
- ✗
SHAP values
Why it's wrong here
SHAP values are outputs of Explainability, not Cloud Monitoring metrics.
- ✓
Prediction latency (p50, p95, p99)
Why this is correct
Correct: Latency percentiles are standard metrics.
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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.