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Difficulty: HardManaging Resource Quotas, Hierarchies, and Cost Optimization

A game development studio is preparing to deploy a global multiplayer backend that requires thousands of GPU-accelerated Compute Engine instances across multiple Google Cloud regions. The platform must also enforce strict cost governance and access control over billing data across distinct development and production folder hierarchies. Which two architectural actions should the Cloud Architect execute to ensure seamless resource provisioning and billing governance? (Select TWO.)

  1. Proactively submit regional GPU quota increase requests via the Google Cloud Console or Service Quotas API well in advance of the scheduled deployment.Answer
  2. Configure Cloud Billing export to a centralized BigQuery dataset and grant fine-grained roles such as Cost Management Viewer and BigQuery Data Viewer to authorized FinOps personnel.Answer
  3. C
    Rely on Compute Engine Managed Instance Group (MIG) autoscaling to dynamically request and increase regional GPU quotas in real time as user traffic surges.
  4. D
    Assign the primitive Owner role to deployment service accounts at the Organization level to allow automated quota modification and billing account configuration.

Answer

The correct architecture requires proactively requesting regional GPU quota increases prior to launch and configuring Cloud Billing data export to BigQuery governed by fine-grained IAM roles.
Proactively requesting regional GPU quota increases ensures that Compute Engine instance groups can scale to the required capacity without encountering quota blockages during launch. Exporting Cloud Billing data to BigQuery while applying predefined least-privilege roles (e.g., Cost Management Viewer) enables comprehensive financial analytics and governance without over-privileging identities.

Step-by-Step Solution

1
Assess quota requirements for GPU accelerators across target regions.
Identified that default regional quotas are insufficient for peak multiplayer load.
Quota increases are not instantaneous and require administrative evaluation by Google Cloud.
2
Submit quota increase requests in advance for the affected regions.
Regional limits are raised prior to deployment, eliminating quota exhaustion risks during autoscaling.
MIG autoscaling can only scale up to the hard quota limit set on the project and region.
3
Establish automated Cloud Billing export to a central BigQuery dataset and enforce granular IAM access controls.
FinOps and operations teams gain real-time cost transparency using least-privilege roles without primitive permission risks.
BigQuery billing export provides complete visibility into resource consumption across folder hierarchies.

Key Concept

Managing Resource Quotas, Hierarchies, and Cost Optimization
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Managing Resource Quotas, Hierarchies, and Cost Optimization