Google Cloud Professional Cloud DevOps Engineer Exam Prep

What's inside

1. Level 1: Exam Orientation and Exam CraftFree
   [Exam orientation] You learn how this course maps to Google's Professional Cloud DevOps Engineer exam guide: the guide is Google's own document, it is free on Google's certification page and it is not reproduced here. You also learn to check Google's official exam page for the current length, price, languages and delivery options, that this course is independent of Google and not endorsed by it, that no pass is promised, and how to approach scenario-based 'best answer' questions.
2. Level 2: Resource Hierarchy and Multi-Project ObservabilityFree
   [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.1] You learn to design the organization, folder and project hierarchy, including application-centric layouts and one project per environment. You also learn to set up multi-project monitoring and logging with Cloud Monitoring metrics scopes and aggregated log sinks at folder or organization level.
3. Level 3: IAM Roles, Organization Policies and Data Residency
   [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.1] You learn how basic, predefined and custom IAM roles are granted down the hierarchy, how organization policy constraints set guardrails, and how to meet data residency requirements with the resource locations constraint and Assured Workloads.
4. Level 4: Creating and Managing Service Accounts
   [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.1] You learn to create service accounts and give each workload its own identity, to use service account impersonation and short-lived credentials instead of exported keys, and to manage and audit service accounts across projects.
5. Level 5: Shared VPC, VPC Peering and Private Service Connect
   [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.1] You learn the three shared-networking patterns in the guide: Shared VPC with host and service projects, VPC Network Peering, and Private Service Connect. You learn the limits of each and when an exam scenario calls for which one.
6. Level 6: Terraform, Infrastructure Manager and Blueprints
   [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.2] You learn to manage infrastructure as code with Terraform the way Google recommends: remote state in Cloud Storage, modules, and plan-before-apply. You also learn to run Terraform through Infrastructure Manager and to start from Cloud Foundation Toolkit blueprints and landing-zone foundations.
7. Level 7: Config Connector, GitOps, Helm and Scripting
   [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.2] You learn the Kubernetes-native ways to manage infrastructure: Config Connector resources in YAML, GitOps with Config Sync, and Helm charts. You also learn when to automate with scripts that use the Python or Go client libraries, and when declarative tooling is the better choice.
8. Level 8: Designing the CI/CD Architecture Stack
   [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.3] You learn to design a CI/CD architecture for Google Cloud, hybrid and multi-cloud environments. Cloud Build handles CI, Cloud Deploy with Skaffold and Kustomize handles CD, and Artifact Registry sits between them. You also learn where third-party tools (Git, Jenkins, Argo CD, Packer, kpt) fit and how to secure the CI/CD tooling itself.
9. Level 9: Managing Ephemeral and Multiple Environments
   [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.4] You learn to run staging, production and short-lived ephemeral environments: per-pull-request environments that are created and torn down automatically, environment-specific configuration, and consistent policy across environments.
10. Level 10: GKE Fleets and Safe Patching and Upgrades
    [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.4] You learn to manage GKE clusters across an enterprise with fleets, and to patch and upgrade safely. That means release channels, maintenance windows and exclusions, surge and blue-green node pool upgrades, rollout sequencing, and VM Manager OS patching for Compute Engine.
11. Level 11: Secure Cloud Dev Environments and Gemini
    [Section 1: Bootstrapping and maintaining a Google Cloud organization – 1.5] You learn to configure Cloud Workstations and Cloud Shell as secure development environments, to bootstrap them with custom container images, IDEs and the Cloud SDK, and to use Gemini Code Assist, Gemini Cloud Assist and Gemini CLI for development and operations.
12. Level 12: Cloud Build Pipelines for Apps and Infrastructure
    [Section 2: Building and implementing CI/CD pipelines – 2.1] You learn to design Cloud Build pipelines in cloudbuild.yaml for both applications and infrastructure: steps, builders, substitutions, artifacts, caching, private pools, and a Terraform plan/apply pipeline.
13. Level 13: CI/CD Pipeline Triggers
    [Section 2: Building and implementing CI/CD pipelines – 2.1] You learn to configure CI/CD pipeline triggers: push and pull-request triggers from connected repositories, manual triggers, Pub/Sub and webhook triggers, and file filters that run only the right pipeline in a monorepo.
14. Level 14: Artifact Management with Artifact Registry
    [Section 2: Building and implementing CI/CD pipelines – 2.1] You learn to manage artifacts with Artifact Registry: standard, remote and virtual repositories, formats, regional placement, IAM per repository, immutable tags, cleanup policies, and moving off Container Registry.
15. Level 15: Cloud Deploy Pipelines and Approval Flows
    [Section 2: Building and implementing CI/CD pipelines – 2.1] You learn to configure deployment processes with Cloud Deploy: delivery pipelines, targets, releases, rollouts and promotion. Approval flows with requireApproval and the Cloud Deploy Approver role gate production, with Skaffold rendering manifests.
16. Level 16: Deploying to GKE, Cloud Run and Hybrid Targets
    [Section 2: Building and implementing CI/CD pipelines – 2.1] You learn to deploy to GKE, Cloud Run and hybrid or multi-cloud clusters: Cloud Deploy target types, GKE attached clusters and fleet membership targets, custom targets, and Skaffold profiles with Kustomize overlays.
17. Level 17: Rolling, Blue/Green, Canary and Feature Flags
    [Section 2: Building and implementing CI/CD pipelines – 2.2] You learn the deployment strategies the exam names: rolling, blue/green, canary, traffic splitting and feature flags. You learn how each works on GKE, Cloud Run and managed instance groups, and how Cloud Deploy canary strategies automate them.
18. Level 18: Success Metrics from App and ML Telemetry
    [Section 2: Building and implementing CI/CD pipelines – 2.2] You learn to define success metrics for a rollout from application or ML pipeline telemetry, such as error rate, latency and model quality. You then wire them into Cloud Deploy verification, deploy hooks and automated advance-or-rollback decisions.
19. Level 19: Auditing and Tracking Deployments
    [Section 2: Building and implementing CI/CD pipelines – 2.2] You learn to audit and track deployments using Artifact Registry metadata, Cloud Build history and provenance, Cloud Deploy release and rollout records, and Cloud Audit Logs. The goal is to answer who deployed what, where and when.
20. Level 20: Troubleshooting and Mitigating Deployment Issues
    [Section 2: Building and implementing CI/CD pipelines – 2.2] You learn to diagnose and mitigate failed deployments: Cloud Build step failures, permission errors, Cloud Deploy render and deploy failures, stuck GKE rollouts (ImagePullBackOff, CrashLoopBackOff, failing readiness probes), Cloud Run revision failures, and fast rollback.
21. Level 21: Cloud KMS, Secret Manager and Secret Injection
    [Section 2: Building and implementing CI/CD pipelines] Serves 2.3: you use Cloud Key Management Service and Secret Manager in pipelines and learn when to inject a secret at build time (Cloud Build availableSecrets) and when to inject it at runtime (Cloud Run and GKE secret mounts), and the leak risks of each.
22. Level 22: Parameter Manager, Certificates and Federation
    [Section 2: Building and implementing CI/CD pipelines] Serves 2.3: you manage non-secret configuration with Parameter Manager and TLS certificates with Certificate Manager, and you let external pipelines such as GitHub Actions, GitLab and Jenkins authenticate with Workload Identity Federation instead of service account keys.
23. Level 23: Artifact Analysis and Vulnerability Scanning
    [Section 2: Building and implementing CI/CD pipelines] Serves 2.4: you turn on automatic and on-demand scanning with Artifact Analysis, read the vulnerability occurrences it records against images in Artifact Registry, and make the pipeline fail when severity is too high.
24. Level 24: Binary Authorization, SLSA and Environment IAM
    [Section 2: Building and implementing CI/CD pipelines] Serves 2.4: you enforce software supply chain security with Binary Authorization attestors and policies, link Cloud Build provenance to SLSA levels, and set IAM policies per environment so that only the pipeline can deploy to production.
25. Level 25: Defining SLIs: Availability and Latency
    [Section 3: Applying site reliability engineering practices] Serves 3.1: you define service level indicators that measure what users actually experience, such as availability and latency. You write each one as a ratio of good events to valid events and pick where to measure it: load balancer, application or synthetic probe.
26. Level 26: Setting SLOs and Understanding SLAs
    [Section 3: Applying site reliability engineering practices] Serves 3.1: you set service level objectives with compliance periods, tell them apart from customer-facing SLAs that carry penalties, and learn why the internal SLO should be stricter than the SLA.
27. Level 27: Error Budgets and Cloud Service Mesh SLOs
    [Section 3: Applying site reliability engineering practices] Serves 3.1: you work out error budgets from SLOs, write an error budget policy that decides when to freeze releases or keep shipping, and define SLOs on Cloud Service Mesh services that Cloud Monitoring detects automatically.
28. Level 28: The Opportunity Cost of Extra Nines
    [Section 3: Applying site reliability engineering practices] Serves 3.1: you weigh the cost of each extra 'nine' of reliability against the feature velocity it gives up, including how the availability of serial dependencies multiplies together and when redundancy pays for itself.
29. Level 29: Service Lifecycle Management
    [Section 3: Applying site reliability engineering practices] Serves 3.2: you manage a service from planning through deployment and maintenance to retirement, including launch readiness reviews, ongoing toil reduction and safe deprecation.
30. Level 30: Capacity Planning: Quotas, Reservations, DWS
    [Section 3: Applying site reliability engineering practices] Serves 3.2: you plan capacity with quotas and limits, Compute Engine reservations and Dynamic Workload Scheduler, so that launches and peak events don't fail for lack of resources.
31. Level 31: Autoscaling: MIGs, Cloud Run and GKE
    [Section 3: Applying site reliability engineering practices] Serves 3.2: you set up autoscaling for managed instance groups, Cloud Run and GKE (HorizontalPodAutoscaler, VerticalPodAutoscaler, cluster autoscaler and node auto-provisioning), and you learn the signals and limits each one responds to.
32. Level 32: Mitigation: Draining, Redirecting, Adding Capacity
    [Section 3: Applying site reliability engineering practices] Serves 3.3: you reduce the impact of an incident on users by draining or redirecting traffic away from a failing zone, region or backend, and by adding capacity quickly. You also learn to mitigate first and find the root cause later.
33. Level 33: Rollback Strategies and Postmortems
    [Section 3: Applying site reliability engineering practices] Serves 3.3: you roll back bad changes with Cloud Deploy rollback, shifting Cloud Run traffic between revisions, kubectl rollout undo and MIG rolling updates, then close the incident with a blameless postmortem.
34. Level 34: Collecting Logs: Ops Agent, OTel, Audit, Flow
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.1: you collect and import logs with the Ops Agent and OpenTelemetry, enable and read Cloud Audit Logs (Admin Activity, Data Access, System Event, Policy Denied), turn on VPC Flow Logs and gather Cloud Service Mesh access logs.
35. Level 35: Optimizing Logs: Exclusions, Sampling and Cost
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.1: you cut log volume and cost with exclusion filters, sampling, and choosing at the source what gets logged, while keeping the logs you need for audits and troubleshooting.
36. Level 36: Collecting Metrics and Managed Prometheus
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.1: you collect metrics from applications, platforms, networking and Cloud Service Mesh, including hybrid and multi-cloud sources, using Google Cloud Managed Service for Prometheus with managed collection on GKE.
37. Level 37: Uptime Checks and Synthetic Monitors
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.1: you create uptime checks and synthetic monitors that probe application endpoints and multi-step workflows before users run into failures.
38. Level 38: Custom Metrics and Log-Based Metrics
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.1: you create custom metrics from application code with OpenTelemetry, and counter and distribution log-based metrics from log entries, and you learn the cardinality and cost pitfalls of each.
39. Level 39: Logs Explorer and the Logging Query Language
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.2: you analyse logs in the Logs Explorer with the Logging query language and use Gemini Cloud Assist to summarise and explain log entries during troubleshooting.
40. Level 40: Routing, Exporting and Retaining Logs
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.2: you export and retain logs by routing them with sinks to BigQuery, Pub/Sub, Cloud Storage and log buckets, setting retention periods, and analysing logs with Log Analytics.
41. Level 41: Metrics Explorer, dashboards and PromQL
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.3: you learn to analyse metrics in Metrics Explorer with filters, grouping and aligners, and to create, filter and share Cloud Monitoring dashboards, including PromQL charts, linked playbooks and dashboards defined as JSON in Terraform. You also learn to use Gemini Cloud Assist to interpret metrics.
42. Level 42: Alerting policies and third-party integrations
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.3: you learn to configure alerting policies on metric thresholds, SLIs and SLO burn rates, and for cost control, and to send notifications to third-party tools through webhooks, PagerDuty and Rootly, while keeping alerts actionable and avoiding alert fatigue.
43. Level 43: Distributed tracing with Cloud Trace and OpenTelemetry
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.4: you learn to instrument services with OpenTelemetry and export traces to Cloud Trace, read trace waterfalls and spans, correlate trace IDs with structured logs, and use Gemini Cloud Assist for trace analysis.
44. Level 44: Troubleshooting infrastructure and CI/CD pipelines
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.5: you learn a repeatable way to troubleshoot infrastructure issues (VM boot and agent failures, GKE node and Pod scheduling problems, networking and IAM denials) and CI/CD pipeline issues (failed Cloud Build steps, Cloud Deploy rollout failures, permission errors), using logs, Cloud Audit Logs and gcloud.
45. Level 45: Troubleshooting application, observability and latency
    [Section 4: Implementing observability practices and troubleshooting issues] Serves 4.5: you learn to troubleshoot application errors, observability gaps (missing logs, metrics or traces caused by agent, scope, exclusion or permission problems) and performance and latency issues, combining Error Reporting, Cloud Trace, Cloud Profiler and the four golden signals.
46. Level 46: Application performance monitoring with Profiler
    [Section 5: Optimizing performance and cost] Serves 5.1: you learn to collect performance information for applications on Google Cloud with Cloud Profiler's continuous CPU and heap profiling, alongside Cloud Trace latency data and Cloud Monitoring service metrics, so that performance work goes where it pays off.
47. Level 47: Active Assist insights and Google Cloud recommenders
    [Section 5: Optimizing performance and cost] Serves 5.1 and 5.2: you learn how Active Assist turns usage data into insights and recommendations across the cost, security, performance, manageability and reliability categories, and how to list, apply and dismiss them with the Recommendation Hub and gcloud recommender.
48. Level 48: FinOps for observability costs
    [Section 5: Optimizing performance and cost] Serves 5.2: you learn what drives Cloud Logging, Cloud Monitoring and Cloud Trace costs (ingestion volume, retention, custom and Prometheus metric volume, high-cardinality labels) and how to reduce them with exclusions, sampling, sink routing and choosing which metrics to collect, without losing the signals you need.
49. Level 49: Spot VMs and rightsizing resource usage
    [Section 5: Optimizing performance and cost] Serves 5.2: you learn to optimise resource usage for cost and efficiency, using Spot VMs for fault-tolerant and batch workloads (including how preemption works and how to design for it) and rightsizing machine types, idle resources and storage based on utilisation data.
50. Level 50: Infrastructure cost planning: CUDs, SUDs, tiers
    [Section 5: Optimizing performance and cost] Serves 5.2: you learn to plan infrastructure costs with resource-based and spend-based committed-use discounts, automatic sustained-use discounts and Premium versus Standard network tiers, and to make cost visible through billing export to BigQuery, labels and budgets.
51. Level 51: Optimizing GKE, Cloud Run and Compute Engine costs
    [Section 5: Optimizing performance and cost] Serves 5.2: you learn to optimise the cost of individual workloads: GKE (Autopilot versus Standard, requests and limits, cluster autoscaler, cost allocation), Cloud Run (CPU allocation, concurrency, minimum instances) and Compute Engine (machine families, autoscaling, scheduling), and to weigh each saving against reliability.
52. Level 52: Timed mock exam (50 questions, 120 minutes) Timed mock
    A timed mock exam of 50 original scenario questions in 120 minutes, drawn from all five exam guide sections in proportion to their weights and testing the whole course at exam difficulty, with a 70% pass mark.

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The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

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