PMLE Monitoring ML Solutions Practice Question
A company wants to track the cost of their Vertex AI prediction endpoint. They use a custom machine type with 1 n1-standard-4 (4 vCPU, 15 GB memory) and 1 NVIDIA T4 GPU. The endpoint is configured for automatic scaling with min=1, max=5 replicas. Which cost monitoring approach should they use?
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 Billing budget alerts and export cost data to BigQuery for analysis.
Vertex AI prediction costs are composed of per-hour per-replica compute and GPU charges plus per-request usage. The best approach is to use Cloud Billing export to BigQuery and query cost data by service and SKU, or use the Vertex AI cost tables in the console.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Cloud Billing budget alerts and export cost data to BigQuery for analysis.
Why this is correct
Correct: Cloud Billing provides accurate cost tracking and alerts.
- ✗
Calculate cost manually based on replica count and GPU hours from endpoint logs.
Why it's wrong here
Manual calculation is error-prone; automated billing export is preferred.
- ✗
Use Vertex AI Experiments to track cost.
Why it's wrong here
Experiments track training job costs, not endpoint serving costs.
- ✗
Monitor only the CPU utilisation metrics to infer cost.
Why it's wrong here
Utilisation does not directly translate to cost; billing data is needed.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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