An enterprise fintech platform processes algorithmic fraud analysis on Google Cloud across multiple projects under a central Cloud Billing account. The operational workload consists of a steady baseline of microservice API calls alongside unpredictable, highly bursty batch jobs during peak financial market trading hours. Currently, all virtual machines operate on standard pay-as-you-go pricing, database resources use standard configurations, and cost reporting is performed manually at month-end.
The FinOps leadership team wants to establish proactive cost governance and automated cost optimization while maintaining high availability for baseline traffic and minimizing financial risk. Which of the following strategies should the team implement? (Select TWO)
- Apply Flexible Committed Use Discounts (CUDs) to cover predictable baseline spend across compute services, and utilize Spot VMs for non-critical bursty batch processing.Answer
- BPurchase 3-year resource-based Committed Use Discounts (CUDs) calculated against peak capacity for all microservices and batch processing instances.
- Enable detailed Cloud Billing export to BigQuery in a dedicated administration project, and configure automated Pub/Sub budget alerts to trigger proactive spend notifications.Answer
- DMigrate standard relational transaction databases to Cloud Spanner across all environments to eliminate operational management fees and lower overall storage billing.
- EGrant primitive Owner IAM roles to automated billing administration service accounts across all child projects to streamline cost metric collection.