Professional Cloud DevOps Engineer
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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:
Which strategy should you implement?
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.
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.
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.
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.
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.
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.
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.
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.
This strategy combines automated machine learning analytics via Google Cloud Recommender, cost-optimized ephemeral compute through Spot VMs, and comprehensive cost modeling tools (Google Cloud Pricing Calculator and Cloud Billing export to BigQuery) to achieve maximum financial efficiency.
This approach directly targets the root causes of cloud waste using native Google Cloud FinOps tools without introducing service disruption or inappropriate commitment locks on ephemeral workloads.
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