PMLE Monitoring ML Solutions Practice Question
An ML engineer is monitoring a Vertex AI Endpoint and notices a spike in 5xx error rates. Which TWO metrics should they examine to diagnose the issue? (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
✓
GPU utilization on the endpoint
CPU/GPU utilization can indicate resource exhaustion causing errors. Prediction job failures metric directly shows failed predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature drift alert count
Why it's wrong here
Drift alerts are separate from error rates.
- ✓
GPU utilization on the endpoint
Why this is correct
GPU exhaustion can lead to prediction failures.
- ✓
CPU utilization on the endpoint
Why this is correct
High CPU may cause timeouts and errors.
- ✗
Vertex AI Model Monitoring skew score
Why it's wrong here
Skew score is for data distribution, not errors.
- ✗
Number of predictions per minute
Why it's wrong here
Prediction volume alone does not indicate errors.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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