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Difficulty: HardOptimizing Business Processes through FinOps and Cloud Cost Management

A smart grid energy utility operates on Google Cloud across 30 projects under a single Cloud Billing account. Their architecture includes baseline regional Compute Engine pools for grid management microservices, unpredictable batch grid load simulation models that run periodically, long-term telemetry analytics on BigQuery, and low-traffic internal portal applications. Recent billing audits show rising operational expenses due to unoptimized resource commitments and unconstrained BigQuery querying. As the Lead Cloud Architect, which TWO cost optimization and FinOps governance strategies should you recommend to minimize expenditure while maintaining operational reliability? (Select TWO)

  1. Purchase Spend-Based Flexible Committed Use Discounts (CUDs) to cover baseline compute expenditure across services, and utilize Spot VMs for fault-tolerant batch grid simulation workloads.Answer
  2. B
    Purchase 3-year standard Resource-Based Committed Use Discounts (CUDs) sized to peak capacity for the batch grid simulation workloads.
  3. Establish BigQuery edition slot reservations with autoscaling slots for analytical workloads, and configure Cloud Billing budgets with programmatic Pub/Sub notifications for automated spend governance.Answer
  4. D
    Migrate historical telemetry analytical datasets from BigQuery to a multi-region Cloud Spanner instance to minimize analytical processing fees and storage costs.
  5. E
    Migrate low-traffic internal portal web applications to dedicated multi-zonal Google Kubernetes Engine (GKE) clusters to centralize container execution.

Answer

The optimal solution requires purchasing Spend-Based Flexible Committed Use Discounts alongside Spot VMs for batch workloads, while implementing BigQuery slot capacity controls combined with Cloud Billing budget alerts via Pub/Sub.
The combination of spend-based Flexible Committed Use Discounts and Spot VMs effectively lowers compute expenses by aligning commitment flexibility with workload tolerance, while BigQuery edition slot reservations and automated Pub/Sub billing budget alerts provide financial predictability and real-time governance across enterprise GCP projects.

Step-by-Step Solution

1
Analyze compute workload profiles to separate baseline continuous utilization from bursty, fault-tolerant batch processing.
Baseline services qualify for Flexible CUDs across compute products, while batch simulations leverage Spot VMs for maximum cost reduction.
Standard resource commitments for peak variable workloads incur wasted spend during off-peak hours.
2
Evaluate analytics cost governance and query optimization.
Transitioning BigQuery from unconstrained on-demand querying to edition slot reservations with autoscaling sets a predictable cost ceiling.
BigQuery slots cap hourly processing expenditure while supporting analytical demand spikes.
3
Implement enterprise-wide FinOps budget tracking and alerting controls.
Cloud Billing budget alerts connected to Pub/Sub enable automated cost governance triggers across all organizational projects.
Programmatic billing notifications allow proactive response to budget burn rate spikes before billing cycles close.

Key Concept

FinOps Compute & Analytics Cost Optimization Strategy
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Topics

Optimizing Business Processes through FinOps and Cloud Cost Management