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Difficulty: HardOptimizing Business Processes through FinOps and Cloud Cost Management

An online gaming enterprise runs its web frontends on Cloud Run, operates dedicated legacy backend engines on Compute Engine instances, and exports massive raw event logs to Cloud Storage. The platform maintains a predictable baseline load throughout the day, supplemented by sharp, unpredictable traffic spikes during live tournament events. The FinOps team requests an organization-wide cost optimization strategy to lower compute and storage expenses while minimizing operational complexity and maintaining low administrative overhead. Which TWO actions should the Cloud Architect recommend?

  1. Purchase spend-based Flexible Committed Use Discounts (CUDs) at the Cloud Billing account level to cover the baseline usage across Cloud Run and Compute Engine.Answer
  2. B
    Purchase 3-year resource-based Committed Use Discounts (CUDs) sized to match peak instance counts during live tournament spikes on Compute Engine.
  3. C
    Migrate the legacy single-region backend databases from Cloud SQL to multi-region Cloud Spanner instances to reduce database operational expenditures.
  4. Configure Cloud Storage Lifecycle Management rules to automatically transition historical raw event log files from Standard to Coldline storage after 30 days.Answer
  5. E
    Migrate all containerized Cloud Run microservices onto a dedicated Google Kubernetes Engine (GKE) cluster to reduce baseline infrastructure costs.

Answer

The Cloud Architect should recommend purchasing spend-based Flexible Committed Use Discounts (CUDs) at the Cloud Billing account level and configuring Cloud Storage Lifecycle Management rules to transition log files from Standard to Coldline storage.
The combination of spend-based Flexible Committed Use Discounts (CUDs) and Cloud Storage Lifecycle Management rules addresses both baseline compute spending across different GCP compute products and long-term storage retention costs without introducing unnecessary management overhead.

Step-by-Step Solution

1
Analyze the compute workload characteristics across services.
The architecture uses both serverless (Cloud Run) and VM-based (Compute Engine) compute, with a steady baseline and unpredictable spikes.
Flexible CUDs provide spend-based discount coverage across compute products (Compute Engine, Cloud Run, GKE), optimizing steady-state spending across diverse compute models without lock-in to specific instance types or regions.
2
Analyze storage lifecycle and cost optimization opportunities.
Raw event logs accumulated in Cloud Storage become infrequently accessed over time.
Automated Object Lifecycle Management rules transition objects from Standard to Nearline or Coldline storage based on age, directly reducing storage tier costs without operational overhead.
3
Evaluate remaining options against operational efficiency and FinOps principles.
Sizing resource-based CUDs to peak loads, migrating regional DBs to Spanner, or moving Cloud Run workloads to GKE increases financial risk or operational overhead.
Distractors introduce unnecessary management complexity or commit funds to transient peak usage.

Key Concept

Optimizing multi-service compute expenses with spend-based Flexible CUDs and automated Cloud Storage lifecycle rules.
Estimated Time:2m 30s
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Topics

Optimizing Business Processes through FinOps and Cloud Cost Management