Finops Rightsizing Recommender Spot Vms Forecasting

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Your enterprise runs a diverse set of workloads on Google Cloud Compute Engine, including core business applications and a large-scale, stateless batch data-processing pipeline. A recent FinOps audit reveals significant cloud spend inefficiencies due to overprovisioned VM instances and standard on-demand pricing across all environments.

You are tasked with implementing a comprehensive cost-optimization strategy that accomplishes the following requirements:

Identify and right-size overprovisioned VM instances using historical utilization metrics.
Maximize cost savings on compute infrastructure for the stateless, fault-tolerant batch pipeline.
Establish an accurate cost estimation and forecasting process for projected infrastructure spend.

Which strategy should you implement?

Configure Cloud Monitoring custom alerts to downsize instances to micro shared-core tiers during off-peak hours, purchase 3-year Committed Use Discounts (CUDs) for the batch pipeline, and use Cloud Billing budget alerts as the primary spend forecasting tool.

Decommission all existing VMs and redeploy onto E2 shared-core instances regardless of performance tier, purchase Flex-start GPU reservations for the batch pipeline, and use the Carbon Footprint tool as the primary spend forecasting mechanism.

Analyze Recommender machine type recommendations evaluated on 30-day P95 utilization metrics to resize overprovisioned VMs, transition the batch pipeline to Spot VMs, and utilize the Google Cloud Pricing Calculator alongside Cloud Billing BigQuery exports to model and forecast costs.

Apply Recommender idle VM recommendations by deleting instances with under 50% average CPU usage, migrate the batch pipeline to standard VMs with 1-year spend-based CUDs, and use the Google Cloud Region Picker to simulate monthly billing reports.

Professional Cloud DevOps Engineer

Finops Rightsizing Recommender Spot Vms Forecasting

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