PMLE Monitoring ML Solutions Practice Question
A team uses Vertex AI Pipelines for continuous training triggered by model drift. They want to monitor the pipeline execution cost and optimize resource usage. Which THREE metrics should they track? (Choose 3)
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
✓
Pipeline execution duration
Training cost is influenced by GPU hours, machine type, and training duration. Tracking these helps optimize.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pipeline execution duration
Why this is correct
Longer duration increases cost; optimizing duration saves money.
- ✗
Number of failed pipeline runs
Why it's wrong here
Failure count is a reliability metric, not cost.
- ✗
Model accuracy on validation set
Why it's wrong here
Accuracy is a quality metric, not cost.
- ✓
Total GPU hours consumed per pipeline run
Why this is correct
GPU hours directly correlate with cost.
- ✓
Cost per pipeline run in Cloud Billing
Why this is correct
Actual cost is the ultimate metric.
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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.