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Difficulty: MediumOptimizing Business Processes through FinOps and Cloud Cost Management

A global logistics provider operates its tracking and route-optimization infrastructure on Google Cloud. The workload environment includes a 24/7 steady-state compute footprint for order tracking services, highly unpredictable batch processing for peak route calculations that runs intermittently for 2–3 hours, and an enterprise BigQuery data warehouse where ad-hoc analytical queries by business analysts frequently cause budget overruns. The FinOps team must establish cost optimization governance that reduces compute spend and caps analytical query costs without degrading operational performance. Which TWO architectural and governance strategies should the team implement? (Select TWO)

  1. Apply 3-year Flexible Committed Use Discounts (CUDs) for the 24/7 steady-state compute baseline, and utilize Spot VMs with autoscaling for the unpredictable batch route calculation workloads.Answer
  2. Configure BigQuery custom cost controls by enforcing daily quota limits on bytes processed per user and project, combined with slot reservations for critical operational analytics workloads.Answer
  3. C
    Purchase 3-year standard Compute Engine Committed Use Discounts dedicated to the unpredictable, short-term batch calculation workloads to guarantee peak capacity discounts.
  4. D
    Migrate the transactional order tracking database from Cloud SQL to Cloud Spanner across multiple regions to lower storage and compute licensing costs during off-peak hours.
  5. E
    Deploy a multi-zone Google Kubernetes Engine (GKE) cluster with fixed non-autoscaling node pools to replace all serverless microservices and eliminate autoscaling latency overhead.

Answer

The team should apply Flexible Committed Use Discounts to cover the 24/7 steady-state compute baseline while leveraging Spot VMs for intermittent batch jobs, and establish BigQuery custom cost controls using daily query byte limits alongside slot reservations for reporting.
Combining Flexible Committed Use Discounts for steady 24/7 baseline compute with Spot VMs for fault-tolerant intermittent batch workloads delivers maximum compute savings. Enforcing BigQuery daily query byte quotas directly prevents budget overruns from unoptimized ad-hoc SQL queries while slot reservations cap reporting costs.

Step-by-Step Solution

1
Analyze compute workload profiles for cost-efficiency
Identified steady 24/7 footprint suited for CUDs and intermittent batch jobs suited for Spot VMs
Matching workload predictability with the correct financial model (CUD vs Spot) maximizes savings without incurring unutilized commitment costs.
2
Establish governance over BigQuery ad-hoc query execution and reporting costs
Configured per-user/per-project daily query byte quotas and slot reservations
Setting byte quotas prevents accidental or runaway query execution costs, while slot reservations provide cost predictability for key business reports.

Key Concept

FinOps Cost Optimization and Compute/Data Governance in GCP
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Optimizing Business Processes through FinOps and Cloud Cost Management