PMLE Monitoring ML Solutions Practice Question
A company is experiencing high prediction costs on Vertex AI Endpoints. They want to monitor and optimize costs. Which THREE actions should they take? (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
✓
Use Cloud Billing reports to track Vertex AI endpoint costs per hour and per request
Cost monitoring involves tracking per-hour and per-request costs, setting budget alerts, and possibly adjusting scaling to reduce unnecessary compute.
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 reports to track Vertex AI endpoint costs per hour and per request
Why this is correct
Cloud Billing provides cost breakdowns by service and resource.
- ✗
Use Vertex AI Explainability on every prediction
Why it's wrong here
Explanations add cost and are not for cost monitoring.
- ✓
Reduce the number of replicas or use autoscaling to minimize idle resources
Why this is correct
Scaling down reduces per-hour costs.
- ✓
Set up budget alerts in Google Cloud Billing to notify when costs exceed a threshold
Why this is correct
Budget alerts help control spending.
- ✗
Enable Vertex AI Model Monitoring to track prediction latency
Why it's wrong here
Latency monitoring does not directly help with cost optimization.
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Same concept, more angles
3 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
easy- ✓ A.Set up Cloud Billing budget alerts and export billing data to BigQuery for analysis
- B.Use Vertex AI Model Monitoring to track cost metrics
- C.Enable Cloud Monitoring dashboards for cost metrics
- D.Use Vertex AI Pipelines to track cost per job
Why A: Vertex AI prediction costs are tracked via Cloud Billing. Budget alerts can be set up to notify when spending exceeds a threshold. Cost breakdown can be viewed in the Billing reports.
Variation 2. An ML engineer needs to track the costs incurred by Vertex AI prediction endpoints. Which tool should they use to set budget alerts and monitor spending?
easy- A.Vertex AI Model Monitoring
- B.Cloud Logging
- C.Cloud Monitoring with custom metrics
- ✓ D.Google Cloud Budgets & Alerts
Why D: Google Cloud Budgets & Alerts allow setting budget thresholds and sending notifications. Billing reports provide cost breakdowns by service.
Variation 3. 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?
medium- ✓ A.Use Cloud Billing budget alerts and export cost data to BigQuery for analysis.
- B.Calculate cost manually based on replica count and GPU hours from endpoint logs.
- C.Use Vertex AI Experiments to track cost.
- D.Monitor only the CPU utilisation metrics to infer cost.
Why A: 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.
JA
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.