GCP Professional Cloud Architect Practice Question
Your finance team reports that several long-running Compute Engine instances in multiple projects are costing more than expected. Operations observes that most of these VMs run at less than 5 % CPU and their attached persistent disks show minimal I/O activity. You must implement an automated workflow that periodically lists such resources, provides specific downsizing or deletion actions, and exports the results to BigQuery for review by the cost-optimization team. Which Google Cloud capability should you use as the authoritative source for those recommendations?
Enable Cloud Billing BigQuery export and query usage and cost tables to infer under-utilized VMs and disks.
Run Cloud Profiler across all instances to detect low CPU activity and use its reports to decide which VMs to resize or delete.
Use the Recommender service's Cost Optimization insights (rightsizing and idle-resource recommendations) and export them to BigQuery.
Collect CPU and I/O metrics via Cloud Monitoring's Metrics API and trigger alerts that flag resources below utilization thresholds.
Google Cloud's Recommender service continuously analyzes resource utilization and cost signals across projects and publishes Cost Optimization insights, including Rightsizing Recommendations for over-provisioned VM machine types and Idle Resource Recommendations for underused VM instances and persistent disks. These insights include concrete actions (resize, stop, delete) and can be accessed programmatically through the Recommender API or exported to BigQuery for automated processing. In contrast, Billing exports and Monitoring metrics provide raw data but do not generate prescriptive resize or cleanup actions, and Cloud Profiler focuses on application-level performance rather than infrastructure rightsizing.
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How can insights from the Recommender service be exported to BigQuery?
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What is the difference between Cloud Monitoring Metrics API and the Recommender service?
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