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Difficulty: MediumOptimizing Business Processes through FinOps and Cloud Cost Management

A smart grid utility enterprise ingests real-time telemetry from millions of sensors into Google Cloud. The core analytical backend relies on a steady, predictable baseline of Compute Engine virtual machines operating continuously 24/7 throughout the year. During severe weather events, compute demand spikes unpredictably up to five times the baseline volume for short durations. The FinOps team must establish a financial governance strategy that minimizes overall compute expenditure while accurately tracking cost attribution across business units. Which strategy best fulfills these requirements?

  1. Purchase Committed Use Discounts (CUDs) scaled to cover the predictable 24/7 baseline compute capacity, handle unpredictable weather spikes using Spot VMs or auto-scaling standard instances, and enforce cost allocation using resource labels exported to BigQuery Billing data.Answer
  2. B
    Purchase 3-year standard Committed Use Discounts sized to cover 100% of the maximum peak capacity observed during worst-case weather events to secure maximum percentage discounts across all instances.
  3. C
    Re-architect all baseline workloads onto a dedicated Google Kubernetes Engine (GKE) cluster using custom multi-zone node pools and Cloud Spanner to eliminate virtual machine billing overhead.
  4. D
    Grant Project Owner primitive IAM roles to individual department leads so they can manually stop unused compute instances and manage billing thresholds directly.

Answer

Purchase Committed Use Discounts scaled to cover predictable baseline capacity, handle unpredictable spikes with Spot VMs or auto-scaling standard instances, and enforce cost allocation via resource labels and BigQuery billing exports.
The optimal strategy combines Committed Use Discounts for the predictable 24/7 baseline workload with elastic mechanisms (Spot VMs or auto-scaling standard instances) for unpredictable weather-driven spikes. Detailed billing exports combined with resource labeling provide the necessary governance for organizational cost attribution.

Step-by-Step Solution

1
Analyze workload baseline versus burst characteristics.
Identified a steady 24/7 baseline demand alongside intermittent, unpredictable 5x usage spikes.
FinOps optimization requires matching commitment models to predictable workloads while maintaining elasticity for burst workloads.
2
Select appropriate pricing structures for each workload tier.
Applied CUDs to the 24/7 baseline capacity and utilized Spot VMs / auto-scaling instances for transient weather spikes.
CUDs offer maximum savings for predictable usage, whereas Spot VMs or dynamic scaling prevent paying for idle commit capacity during non-spike periods.
3
Implement governance and cost attribution mechanisms.
Enforced metadata labeling on resources and enabled detailed billing exports to BigQuery.
Provides visibility into expenditure by business unit and allows accurate cost allocation.

Key Concept

FinOps Cost Governance and Workload-based Commitment Strategies
Estimated Time:1m 30s
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Topics

Optimizing Business Processes through FinOps and Cloud Cost Management