GCP DevOps Engineer study materials
GCP DevOps Engineer study resources

GCP DevOps Engineer case studies

GCP DevOps Engineer · Put your knowledge to work in context.

The Professional Cloud DevOps Engineer is a Google Cloud certification for practitioners who implement processes and capabilities across the systems development lifecycle using Google-recommended methodologies and tools, enabling efficient software and infrastructure delivery while balancing reliability with delivery speed. It suits engineers with 3+ years of industry experience, including at least 1+ year designing and managing production systems on Google Cloud; there are no prerequisites. The GCP DevOps Engineer case studies are 5 cases and about 3,849 words.

1

Guardrails in a two-organization merger

Northwind Payments has run on Google Cloud for three years with a single organization, 14 projects, and no folders. It has just acquired a 90-person lender that has its own Google Cloud organization, six projects, and a PCI-scoped card-processing workload.

741 words, about 4 min
2

The SLO that stayed green through 41 minutes of downtime

TicketNova sells concert tickets. The purchase path is a Cloud Run front end, a GKE checkout service, and an HA Cloud SQL primary.

783 words, about 4 min
3

A 47-minute pipeline that keeps shipping bad code

Helios Health runs a 60-service monorepo on GitHub, built by Cloud Build triggers and deployed to dev, staging, and prod GKE clusters. Every build runs the full unit and integration suite and takes 47 minutes; developers merge about 35 PRs a weekday and the median PR waits 40 minutes for a verdict.

770 words, about 4 min
4

3.8 TiB a day and nobody can find the slow request

FleetPulse ingests telemetry from 240,000 delivery vans into a GKE Autopilot service at about 9,000 requests per second. Cloud Logging takes 3.8 TiB a day and is now the second-largest line item on the bill.

808 words, about 4 min
5

GPUs idle 69% of the time and imagery nobody reads

Orbital Analytics runs GPU training on a GKE Standard cluster with a fixed pool of 96 a2-highgpu-1g nodes. GPU utilization averages 31% and saturates only during two four-hour windows each week; training jobs must finish within an eight-hour nightly window.

747 words, about 4 min