PMLE Monitoring ML Solutions Practice Question
An ML engineer needs to monitor the online prediction latency of a Vertex AI Endpoint. Which metrics should they look at in Cloud Monitoring?
⚠ Common exam trap
PMLE often tests the difference between latency metrics and other monitoring metrics, and candidates may confuse latency with throughput or resource utilization.
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
✓
p50, p95, p99 latency
To monitor online prediction latency on a Vertex AI Endpoint, the engineer should look at percentile latency metrics such as p50, p95, and p99. These metrics provide a distribution of latency, showing typical and tail latencies, which are crucial for understanding user experience and identifying outliers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
p50, p95, p99 latency
Why this is correct
Percentile latencies (p50, p95, p99) expose the tail behaviour that averages hide, which matters for online prediction where a minority of slow requests breach user-facing SLAs. Vertex AI publishes these under `prediction/latencies` in Cloud Monitoring, directly satisfying the requirement to monitor endpoint latency distribution rather than throughput or resource saturation.
- ✗
Request count and error rate
Why it's wrong here
Request count and error rate quantify traffic volume and failed requests, not the elapsed time per prediction. They are tempting because they are standard endpoint metrics and would be correct when measuring throughput or diagnosing HTTP 5xx failures returned by the endpoint.
- ✗
Skew and drift scores
Why it's wrong here
Skew and drift scores compare training and serving data distributions, not request latency. They are tempting because they are Vertex AI Model Monitoring metrics and would be correct when detecting feature distribution shift or training-serving skew that degrades prediction quality over time.
- ✗
CPU/GPU utilization
Why it's wrong here
CPU/GPU utilization monitors resource usage but not prediction latency.
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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.