GCP Professional Cloud Architect Practice Question

Your organization hosts dev, test, and production workloads in distinct Google Cloud projects under one Cloud Billing account. Finance requires that when aggregated monthly spend hits 90 % of a US$50 000 budget, the FinOps mailing list is notified. At 100 % spend, every Compute Engine VM in the dev and test projects must shut down automatically while production stays running. You want a low-overhead solution using only managed Google Cloud services. What should you do?

Configure a Cloud Monitoring alert on the billing/gcp_cost metric at 90 % and 100 %; have the alert trigger a Cloud Run job that shuts down all non-production VMs.
Purchase a US$50 000 monthly Committed Use Discount for dev and test workloads and export billing data to BigQuery for manual review of overruns.
Set organization-level Compute Engine quotas for dev and test projects to zero once overall spend reaches 100 % using an Organization Policy constraint.
Create a single Cloud Billing Budget with 90 % email and 100 % Pub/Sub thresholds; trigger a Cloud Function that calls the Cloud Billing and Compute Engine APIs to disable billing and stop VMs in dev and test projects.

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Answer Description

Create a single Cloud Billing Budget that covers all projects and sets two threshold rules: one at 90 % with email notifications to the FinOps list, and one at 100 % that publishes a message to a Cloud Pub/Sub topic. Deploy a Cloud Function subscribed to that topic. When triggered, the function calls the Cloud Billing API to disable billing (or set the spending limit to zero) on the dev and test projects and uses the Compute Engine API to stop their VM instances. This relies entirely on managed services-Budgets, Pub/Sub, Cloud Functions, and Cloud APIs-fulfilling both the alerting and automated remediation requirements without introducing additional infrastructure.

Other options fall short:

Relying solely on Cloud Monitoring alerts cannot enforce hard spend stops and does not integrate directly with budgets.
Purchasing CUDs and exporting data to BigQuery offers no enforcement or timely alerting.
Manipulating quotas or policy constraints does not correlate with real-time spend and cannot guarantee shut-down exactly at 100 % of the budget.

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