PMLE Monitoring ML Solutions Practice Question
A company wants to monitor the cost of their Vertex AI prediction endpoint. They are charged per hour per replica and per request for GPU instances. Which approach should they use to track these costs?
⚠ Common exam trap
PMLE often tests the confusion between operational monitoring (Cloud Monitoring) and cost monitoring (Cloud Billing) — candidates pick Cloud Monitoring dashboards assuming cost metrics appear there, but billing data requires the Billing export.
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
✓
Set up Cloud Billing budget alerts and export billing data to BigQuery for analysis
Cloud Billing budget alerts plus BigQuery billing export is the standard GCP approach for tracking and analyzing Vertex AI endpoint costs. Budget alerts notify when spend crosses thresholds, and the detailed billing export to BigQuery enables granular analysis by SKU, label, and resource — including per-replica and per-request GPU charges.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set up Cloud Billing budget alerts and export billing data to BigQuery for analysis
Why this is correct
Cloud Billing export to BigQuery captures per-replica hourly GPU charges and per-request costs as granular line items, letting you query and attribute spend by endpoint. Budget alerts alone only notify on thresholds; BigQuery analysis satisfies the requirement to track both billing dimensions.
- ✗
Use Vertex AI Model Monitoring to track cost metrics
Why it's wrong here
Model Monitoring detects training-serving skew, drift and feature anomalies in predictions; it exposes no billing or usage metrics. Endpoint replica-hour and request charges require Cloud Billing export with resource labels. It tempts because both attach to endpoints, but monitoring observes model behaviour, not expenditure.
- ✗
Enable Cloud Monitoring dashboards for cost metrics
Why it's wrong here
Cloud Monitoring dashboards surface operational metrics such as latency, error rates and resource utilisation, not the billed replica-hour and per-request GPU charges. Cost tracking needs Cloud Billing export to BigQuery with labels. It tempts because dashboards visualise endpoint telemetry, but billing data lives in Cloud Billing.
- ✗
Use Vertex AI Pipelines to track cost per job
Why it's wrong here
Vertex AI Pipelines tracks training and pipeline job execution, not endpoint serving charges billed per replica-hour and per request. Endpoint cost attribution needs billing labels and Cloud Billing export. It tempts because pipelines do surface job costs, but prediction endpoints are not pipeline jobs.
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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.