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Difficulty: Very hardOptimizing Business Processes through FinOps and Cloud Cost Management

A global logistics provider is establishing a enterprise-wide FinOps governance framework across 60 GCP projects. The current infrastructure consists of three main workload patterns: a core suite of legacy enterprise applications running 24/7 on Compute Engine with stable baseline CPU utilization; an unpredictable batch data processing workflow using BigQuery; and multiple transient, low-traffic web microservices for internal dev/test environments deployed on standard GKE clusters. The Chief Financial Officer requires a strategy that minimizes waste, establishes accurate departmental chargeback, and optimizes overall cloud spend without sacrificing operational flexibility. Which combination of cost optimization and governance practices should the Cloud Architect recommend?

  1. Purchase Compute Committed Use Discounts (CUDs) for the predictable 24/7 Compute Engine baseline, migrate transient low-traffic internal web microservices to Cloud Run to eliminate idle cluster overhead, utilize BigQuery Enterprise edition autoscaling slots, and implement mandatory resource labeling tied to BigQuery Billing exports for automated chargeback.Answer
  2. B
    Purchase 3-year standard Committed Use Discounts matching peak aggregate capacity across all GKE dev/test nodes, retain on-demand BigQuery pricing, and assign Owner primitive IAM roles to team leads so they can modify resource sizing directly in the GCP console.
  3. C
    Migrate all legacy application databases and batch data analytics storage to a single global multi-region Cloud Spanner instance to centralize billing management and leverage volume discounts across all projects.
  4. D
    Consolidate all workloads—including transient dev/test services and legacy batch jobs—into a large, statically provisioned regional GKE cluster running 24/7 to aggregate resource usage under a single project invoice.

Answer

The correct strategy combines Compute Committed Use Discounts (CUDs) for the steady 24/7 baseline VMs, migrates transient dev/test microservices to serverless Cloud Run to avoid idle node costs, utilizes BigQuery slot autoscaling for variable batch jobs, and enforces resource labels paired with BigQuery billing export for departmental chargeback.
The solution properly maps each architectural pattern to its optimal FinOps pricing mechanism. Stable 24/7 workloads benefit from Compute CUDs for heavy baseline savings. Low-traffic transient microservices incur zero idle costs when run on Cloud Run due to scaling to zero. Variable analytics workloads avoid peak on-demand costs through BigQuery slot autoscaling. Standardized labels coupled with BigQuery billing export fulfill the organizational chargeback governance requirement.

Step-by-Step Solution

1
Analyze workload resource patterns
Identified steady-state 24/7 VM usage, unpredictable batch processing, and transient/low-traffic dev/test web services.
Different compute and data patterns require distinct pricing and platform models to optimize cost efficiency.
2
Select commitment and compute platforms
Apply Compute CUDs to the 24/7 baseline VMs, transition transient web services from GKE to serverless Cloud Run, and use BigQuery edition slot autoscaling.
CUDs offer substantial discounts on stable baselines, Cloud Run scales to zero during idle periods, and slot autoscaling avoids paying high on-demand rates or over-provisioning slots for bursty BigQuery queries.
3
Establish FinOps governance and chargeback
Implement mandatory resource labeling and export detailed Cloud Billing data to BigQuery.
Granular billing export combined with standardized key-value labels enables accurate multi-project cost allocation and chargeback reporting.

Key Concept

FinOps Cloud Cost Optimization and Governance Frameworks
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Topics

Optimizing Business Processes through FinOps and Cloud Cost Management