Professional Cloud DevOps Engineer
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Your team manages a multi-tier production web application running across Compute Engine Managed Instance Groups (MIGs) and Google Kubernetes Engine (GKE) clusters. Active Assist generates several cost optimization recommendations, identifying overprovisioned GKE node pools (CLUSTER_OVERPROVISIONED) and underutilized VM machine types.
You need to apply these remediation actions safely and validate that the changes successfully reduce cloud spending without degrading application reliability or user experience.
Which end-to-end strategy should you execute?
Downsize the machine types immediately across all production instances, and validate the effectiveness exclusively by verifying that CPU and memory utilization metrics in Cloud Monitoring increase to over 90%.
Manually delete running VM instances and terminate individual GKE worker nodes using kubectl to force downscaling, then rely on Cloud Billing budget overrun alerts to confirm whether costs decrease.
Update the MIG instance templates and GKE node pool machine specifications to rightsize compute resources, then monitor user-facing SLIs (such as latency and error rates) in Cloud Monitoring alongside cost trends in Cloud Billing reports and BigQuery exports to validate the changes.
Configure Horizontal Pod Autoscaling (HPA) for application deployments while leaving node pools unchanged, and validate the changes by checking that the Recommender state updates to 'Applied' in the Active Assist console.
Downsize the machine types immediately across all production instances, and validate the effectiveness exclusively by verifying that CPU and memory utilization metrics in Cloud Monitoring increase to over 90%.
Manually delete running VM instances and terminate individual GKE worker nodes using kubectl to force downscaling, then rely on Cloud Billing budget overrun alerts to confirm whether costs decrease.
Update the MIG instance templates and GKE node pool machine specifications to rightsize compute resources, then monitor user-facing SLIs (such as latency and error rates) in Cloud Monitoring alongside cost trends in Cloud Billing reports and BigQuery exports to validate the changes.
Implementing remediation from Active Assist recommendations requires modifying declarative infrastructure configurations—such as Managed Instance Group (MIG) instance templates and GKE node pool machine types—followed by continuous validation using both operational observability and financial reporting tools.
Cloud optimization is an iterative process of assessment, implementation, and re-measurement. Applying Active Assist recommendations via formal configuration templates while measuring user-centric SLIs alongside detailed billing analytics provides the complete feedback loop required to confirm both technical health and financial efficiency.
Configure Horizontal Pod Autoscaling (HPA) for application deployments while leaving node pools unchanged, and validate the changes by checking that the Recommender state updates to 'Applied' in the Active Assist console.
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