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Zorluk: OrtaOptimizing Business Processes through FinOps and Cloud Cost Management

An agricultural technology enterprise operates a global IoT telemetry processing platform on Google Cloud. The infrastructure consists of a steady 24/7 baseline stream processing pipeline, unpredictable bursty batch processing jobs for sudden weather event analytics, and raw data storage used for analytical queries in BigQuery. The central FinOps team wants to optimize cloud expenditure without compromising processing performance or system security. Which TWO cost optimization strategies should the Cloud Architect recommend? (Select TWO)

  1. Apply Flexible Committed Use Discounts (Flex CUDs) to cover baseline compute resources, and utilize Spot VMs for the fault-tolerant, unpredictable weather batch processing jobs.Cevap
  2. Implement Cloud Storage Lifecycle Management to transition raw telemetry logs to Nearline or Coldline storage classes after 30 days, and use BigQuery slot capacity management for predictable analytics workloads.Cevap
  3. C
    Purchase 3-year standard Compute Engine Committed Use Discounts (CUDs) sized to match peak compute capacity during bursty weather events.
  4. D
    Migrate all regional relational telemetry databases from Cloud SQL to multi-region Cloud Spanner instances to achieve automatic resource cost reductions.
  5. E
    Grant Project Owner IAM roles to lead engineers so they can manually delete unattached persistent disks and idle virtual machines.

Cevap

The Cloud Architect should recommend purchasing Flexible Committed Use Discounts (Flex CUDs) for baseline compute combined with Spot VMs for bursty batch jobs, alongside setting Cloud Storage Lifecycle Management policies and BigQuery slot capacity controls.
Combining Flexible Committed Use Discounts (Flex CUDs) for baseline compute with Spot VMs for fault-tolerant batch workloads addresses compute cost efficiency across steady and bursty demands. Concurrently, automating Cloud Storage object lifecycle management transitions less frequently accessed telemetry data to colder storage tiers, while BigQuery slot reservations prevent unexpected query cost spikes.

Adım Adım Çözüm

1
Analyze compute workload profiles
Identify 24/7 stream processing as steady baseline and weather analytics as bursty and fault-tolerant.
Baseline workloads benefit from commitment discounts, whereas bursty, stateless workloads are ideal for low-cost Spot instances.
2
Evaluate storage and analytics cost optimization mechanisms
Apply lifecycle rules to move aging telemetry data to colder storage classes and control BigQuery querying costs with slot management.
Data access frequency decreases over time, making lifecycle automation a direct driver of storage cost reduction.
3
Identify and eliminate anti-patterns in distractors
Reject purchasing standard CUDs for peak capacity, migrating to Cloud Spanner, or granting Owner IAM roles.
These anti-patterns lead to wasted commitment spend, unnecessary architecture overhead, and security compliance violations.

Anahtar Kavram

FinOps multi-layered cost optimization utilizing flexible commitments, Spot instances, storage lifecycle automation, and slot governance.
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Konular

Optimizing Business Processes through FinOps and Cloud Cost Management