Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!
Prepare and test your skills
Prepare and test your skills
Worked example. The correct answer is already marked and every option is explained below, so there is nothing to select here. To answer questions yourself, start the free trial.
Keep the momentum going with these hand-picked practice scenarios
Want more questions like this?
Get a free certification question every week.
Last updated
An enterprise organization runs hundreds of workloads on Compute Engine virtual machines and Cloud SQL instances across dozens of projects in Google Cloud. The finance and FinOps teams report an unexpected increase in monthly spend and identify two core operational requirements:
Which strategy should the cloud architect recommend to meet these requirements?
Enable the Service Usage API across all projects, assign project-level labels only, and rely exclusively on Cloud Billing reports to identify underutilized compute instances.
Create IAM conditional policies using Resource Manager tags for cost center allocation, and query the Service Limit Recommender API to identify overprovisioned VM CPU and memory configurations.
Enable the Recommender API in a central billing project, query organizational-level recommenders using the billing-project flag, and apply standard labels (such as costcenter and environment) directly to individual resources for Cloud Billing report filtering.
Enable the Recommender API in each individual workload project, attach network tags to the virtual machine instances, and filter the Cloud Billing reports by network tag.
Enable the Service Usage API across all projects, assign project-level labels only, and rely exclusively on Cloud Billing reports to identify underutilized compute instances.
Create IAM conditional policies using Resource Manager tags for cost center allocation, and query the Service Limit Recommender API to identify overprovisioned VM CPU and memory configurations.
Enable the Recommender API in a central billing project, query organizational-level recommenders using the billing-project flag, and apply standard labels (such as costcenter and environment) directly to individual resources for Cloud Billing report filtering.
This architecture leverages the Recommender API from a centralized project in conjunction with granular resource labeling to achieve cross-project cost visibility, right-sizing, and cost attribution.
StudyCerts
StudyCerts--billing-project flag (or x-goog-user-project header) in Google Cloud CLI or REST API requests, administrators can query recommendations and utilization insights across all projects in the entire organization (such as google.compute.instance.MachineTypeRecommender, google.cloudsql.instance.OverprovisionedRecommender, and google.resourcemanager.projectUtilization.Recommender).costcenter=finance or environment=production) directly to individual VMs and storage resources ensures usage metadata is forwarded directly to the Cloud Billing system, allowing finance teams to filter and group costs by label in Cloud Billing reports and BigQuery billing exports.Using a single billing project to query organization-wide recommendations eliminates management sprawl and API configuration overhead. Combining this with standard key-value labels on resources directly enables exact cost tracking across different cost centers in Cloud Billing reports.
Enable the Recommender API in each individual workload project, attach network tags to the virtual machine instances, and filter the Cloud Billing reports by network tag.