PMLE Monitoring ML Solutions Practice Question
A team is monitoring a model on Vertex AI Endpoints and wants to track the p99 latency of online predictions. Which approach should they use to set up latency monitoring and alerting?
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
✓
Use Cloud Logging to create log-based metrics from prediction logs and set up alerts in Cloud Monitoring
Vertex AI Endpoints automatically export request/response logs to Cloud Logging, which can be used to create log-based metrics for latency percentiles. These metrics can then be visualized in Cloud Monitoring dashboards and used for alerting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Vertex AI Model Monitoring and select 'latency' as a metric
Why it's wrong here
Model Monitoring does not track latency; it focuses on data drift.
- ✗
Enable Vertex AI Explainable AI to output latency statistics
Why it's wrong here
Explainable AI provides feature attributions, not latency metrics.
- ✗
Configure Cloud Monitoring to scrape Prometheus metrics from the endpoint
Why it's wrong here
Vertex AI Endpoints do not expose Prometheus metrics directly; logs are the recommended source.
- ✓
Use Cloud Logging to create log-based metrics from prediction logs and set up alerts in Cloud Monitoring
Why this is correct
Prediction logs contain latency information; log-based metrics can capture p99 and other percentiles.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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