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

A multinational fintech company operates a decentralized GCP organization with over 120 projects. The FinOps governing council observes escalating cloud costs across three major areas: predictable 24/7 Compute Engine virtual machines supporting core services, unpredictable ad-hoc BigQuery analytical queries run by risk analysts, and multi-terabyte application logs retained indefinitely in standard Cloud Storage buckets. The Enterprise Architect must establish a comprehensive cost optimization strategy that preserves workload performance while strictly reducing waste. Which architectural and governance actions should the team implement? (Select THREE answers.)

  1. Purchase Flexible Committed Use Discounts (Flexible CUDs) for Compute Engine to cover baseline spend across different machine types and regions.Answer
  2. Transition BigQuery project billing from on-demand pricing to Edition-based slot capacity reservations with slot autoscaling enabled for analytical workloads.Answer
  3. Configure Cloud Storage Object Lifecycle Management policies to transition older log files from Standard storage to Coldline or Archive storage classes based on object age.Answer
  4. D
    Purchase 3-year resource-based Committed Use Discounts sized to match peak burst memory and CPU capacity across all GKE node pools.
  5. E
    Migrate application log archives from Cloud Storage into a multi-region Cloud Spanner instance to reduce storage costs via built-in global indexing.

Answer

The team should implement Flexible Committed Use Discounts for Compute Engine baseline compute, transition BigQuery to Edition slot reservations with autoscaling, and apply Cloud Storage Object Lifecycle Management policies for log archives.
Optimizing GCP expenditure across a large enterprise requires targeted cost reduction mechanisms: Flexible CUDs cover baseline compute across diverse machine families and regions; BigQuery slot reservations with autoscaling contain query cost variability; and Cloud Storage lifecycle policies automate tiering of archival log data to cheaper storage classes.

Step-by-Step Solution

1
Analyze compute spending patterns
Identify that baseline compute across multi-region workloads can be optimized using dollar-based Flexible CUDs rather than rigid instance-bound commitments.
Flexible CUDs allow spending commitments to span regions and machine types, offering maximum flexibility for dynamic enterprise workloads.
2
Analyze analytical query cost governance
Shift BigQuery from variable on-demand per-TB pricing to Edition slot reservations with autoscaling.
Slot reservations establish predictable cost caps while autoscaling accommodates demand spikes without over-provisioning flat-rate capacity.
3
Analyze long-term storage cost retention
Implement automated Cloud Storage Object Lifecycle Management rules.
Transitioning aging logs to Coldline or Archive tiers minimizes storage unit costs automatically based on retention age.

Key Concept

FinOps Cost Optimization and Resource Governance in GCP
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Topics

Optimizing Business Processes through FinOps and Cloud Cost Management