Professional Cloud DevOps Engineer
Certification exam guide
A Professional Cloud DevOps Engineer implements processes and capabilities throughout the systems development lifecycle using Google-recommended methodologies and tools. They enable efficient software and infrastructure delivery while balancing reliability with delivery speed. They optimize and maintain production systems and services.
This version of the Professional Cloud DevOps Engineer exam places less emphasis on the cultural components of Site Reliability Engineering (SRE) than the previous version.
Section 1: Bootstrapping and maintaining a Google Cloud organization (~15% of the exam)
1.1 Designing the overall resource hierarchy for an organization.
Considerations include: ● Projects and folders ● Shared networking ● Multi-project monitoring
and logging
● Identity and Access Management (IAM)
roles and organization-level policies ● Creating and managing service accounts ● Organizing resources by using an
application-centric approach (e.g., App Hub) 1.2 Managing infrastructure.
Considerations include: ● Infrastructure-as-code tooling
(e.g., Cloud Foundation Toolkit, Config Connector, Terraform, Helm) ● Making infrastructure changes
using Google-recommended practices and blueprints ● Automation with scripting
(e.g., Python, Go) 1.3 Designing a CI/CD architecture stack in Google Cloud,
hybrid, and multi-cloud environments.
Considerations include: ● Continuous integration (CI)
with Cloud Build ● Continuous delivery (CD)
with Cloud Deploy, including Kustomize and Skaffold ● Widely used third-party tooling
(e.g., Jenkins, Git, Argo CD, Packer) ● Security of CI/CD tooling
1.4 Managing multiple environments
(e.g., staging, production).
Considerations include: ● Determining the number of
environments and their purpose ● Managing ephemeral environments ● Configuration and policy management ● Managing Google Kubernetes Engine (GKE)
clusters across an enterprise ● Safe and secure patching and upgrading
practices 1.5 Enabling secure cloud development environments.
Considerations include: ● Configuring and managing cloud development
environments (e.g., Cloud Workstations, Cloud Shell) ● Bootstrapping environments with required
tooling (e.g., custom images, IDE, Cloud SDK) ● Leveraging AI to assist with development
and operations (e.g., Cloud Code, Gemini Code Assist)
Section 2: Building and implementing CI/CD pipelines for applications and infrastructure (~27% of the exam)
2.1 Designing and managing CI/CD pipelines.
Considerations include: ● Artifact management with
Artifact Registry ● Deployment to hybrid and multi-cloud
environments (e.g., GKE Enterprise) ● CI/CD pipeline triggers
● Testing a new application
version in the pipeline ● Configuring deployment
processes (e.g., approval flows) ● CI/CD of serverless applications ● Applying CI/CD practices to
infrastructure (e.g., GKE clusters, managed instance groups,
Cloud Service Mesh configuration) 2.2 Implementing CI/CD pipelines.
Considerations include: ● Auditing and tracking
deployments (e.g., Artifact Registry, Cloud Build,
Cloud Deploy, Cloud Audit Logs)
● Deployment strategies
(e.g., canary, blue/green, rolling, traffic splitting)
● Troubleshooting and mitigating
deployment issues 2.3 Managing CI/CD configuration and secrets.
Considerations include: ● Key management (e.g., Cloud Key
Management Service) ● Secret management
(e.g., Secret Manager, Certificate Manager) ● Build versus runtime secret injection 2.4 Securing the CI/CD deployment pipeline.
Considerations include: ● Vulnerability analysis
with Artifact Registry ● Software supply chain security
(e.g., Binary Authorization, Supply-chain Levels for
Software Artifacts [SLSA] framework) ● IAM policies based on environment
Section 3: Applying site reliability engineering practices to applications (~23% of the exam)
3.1 Balancing change, velocity, and reliability of
the service. Considerations include: ● Defining SLIs (e.g., availability, latency),
SLOs, and SLAs ● Error budgets ● Opportunity cost of risk and reliability
(e.g., number of "nines") 3.2 Managing service lifecycle.
Considerations include: ● Service management (e.g., introduction
of a new service by using a pre-service onboarding checklist, launch
plan, or deployment plan, deployment, maintenance, and retirement) ● Capacity planning (e.g., quotas, limits) ● Autoscaling (e.g., managed
instance groups, Cloud Run, GKE) 3.3 Mitigating incident impact on users.
Considerations include: ● Draining/redirecting traffic ● Adding capacity ● Rollback strategies
Section 4: Implementing observability practices (~20% of the exam)
4.1 Managing logs. Considerations include: ● Collecting and importing logs
(e.g., Cloud Logging agent, Cloud Audit Logs, VPC Flow Logs,
Cloud Service Mesh) ● Logging optimization
(e.g., filtering, sampling, exclusions, cost, source
considerations) ● Exporting logs
(e.g., BigQuery, Pub/Sub, for auditing) ● Retaining logs ● Analyzing logs ● Handling sensitive data
(e.g., personally identifiable information [PII], protected
health information [PHI]) 4.2 Managing metrics.
Considerations include: ● Collecting and analyzing metrics
(e.g., application, platform, networking, Cloud Service Mesh,
Google Cloud Managed Service for Prometheus, hybrid/multi-cloud) ● Creating custom metrics from logs ● Using Metrics Explorer for ad hoc
metric analysis ● Creating synthetic monitors 4.3 Managing dashboards and alerts.
Considerations include: ● Managing dashboards
(e.g., creating, filtering, sharing, playbooks) ● Configuring alerting and
alerting policies (e.g., SLIs, SLOs, cost control) ● Widely used third-party alerting tools
Section 5: Optimizing performance and troubleshooting (~15% of the exam)
5.1 Troubleshooting issues.
Considerations include: ● Infrastructure issues
● Application issues ● CI/CD pipeline issues ● Observability issues ● Performance and latency issues 5.2 Implementing debugging tools in Google
Cloud. Considerations include: ● Application instrumentation ● Cloud Trace ● Error Reporting 5.3 Optimizing resource utilization and
costs. Considerations include: ● Observability costs ● Spot virtual machines (VMs) ● Infrastructure cost planning
(e.g., committed-use discounts, sustained-use discounts,
network tiers)
● Google Cloud recommenders
(e.g., cost, security, performance, manageability,
reliability)