A media streaming company runs an unpredictable, high-volume batch video processing workload alongside a steady-state REST API service on Google Cloud. The finance and platform engineering teams need to establish a FinOps governance framework to reduce cloud expenditures while maintaining automated budget tracking across multiple projects. Which of the following strategies should a Cloud Architect recommend? (Select TWO)
- Utilize Spot VMs for the batch video processing workloads and purchase flexible Committed Use Discounts (CUDs) to cover baseline compute spend.Answer
- Export detailed Cloud Billing data to BigQuery and configure programmatic budget notifications using Pub/Sub to trigger automated cost-control actions.Answer
- CPurchase 3-year standard resource-based Committed Use Discounts (CUDs) specifically dedicated to the batch video processing compute instances.
- DMigrate standard relational backend databases from Cloud SQL to Cloud Spanner to minimize base instance running costs.
- EDeploy stateless REST API services to Google Kubernetes Engine (GKE) clusters with dedicated node pools instead of Cloud Run to reduce serverless overhead.
Answer
The correct recommendations are to utilize Spot VMs for batch video processing alongside flexible Committed Use Discounts (CUDs) for baseline spend, and to export Cloud Billing data to BigQuery with Pub/Sub budget alerts for automated governance.
Combining Spot VMs for interruption-tolerant batch processing with flexible spend-based CUDs ensures maximum savings across both variable and steady-state compute. Concurrently, exporting billing metrics to BigQuery and integrating Pub/Sub budget alerts creates a robust FinOps control loop capable of programmatic remediation.
Step-by-Step Solution
Key Concept
FinOps Cost Optimization and Governance in GCP