Google Cloud Professional Cloud DevOps Engineer Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Bootstrapping and maintaining a Google Cloud organization (15% of the exam)

Designing the overall resource hierarchy for an organization.

• 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)

Summary: In this section, you will focus on how to design a structured and effective resource hierarchy within Google Cloud. Understanding the relationship between projects, folders, and organizations, along with how shared networking and IAM policies apply across these levels, is key. Attention is given to service accounts and best practices for application-centric organization using tools like App Hub.

This ensures that environments remain scalable and secure while promoting efficient collaboration. With a strong grasp of resource hierarchy, you will be able to optimize project and folder usage for enterprise-scale systems while aligning authorization and access control with organizational policies, creating a secure and well-governed environment.

Managing infrastructure.

• 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)

Summary: This section highlights infrastructure as code (IaC) and automation strategies. It delves into industry-standard tools like Terraform, Helm, and Config Connector, showing you how to standardize, automate, and manage infrastructure in reproducible and version-controlled ways. Scripts in languages like Go and Python provide further customization while aligning with Google-recommended practices and reusable blueprints.

Building infrastructure through IaC not only improves predictability but also ensures that cloud environments can scale seamlessly and adapt to organizational policies. This approach cultivates resilience against manual errors, accelerates provisioning cycles, and simplifies future expansions.

Designing a CI/CD architecture stack in Google Cloud, hybrid, and multi-cloud environments.

• 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

Summary: A strong aspect of modern DevOps is an efficient CI/CD stack, and this section ensures you can plan one across hybrid and multi-cloud environments. By integrating Google Cloud-native services like Cloud Build and Cloud Deploy with popular third-party tools like Jenkins, Git, and Argo CD, you develop pipelines that are secure, repeatable, and built to support rapid delivery cycles.

Security remains a key focus, ensuring that CI/CD tooling configurations uphold enterprise standards. This balance creates a technology ecosystem that operates reliably at scale while empowering development teams to achieve velocity without sacrificing governance.

Managing multiple environments (e.g., staging, production).

• 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

Summary: This section teaches you how to manage environments for different stages of application lifecycle. It highlights when to create environments like staging or pre-production, how to handle ephemeral setups created for short-term purposes, and how configuration and policies are managed consistently across environments.

The section also covers advanced topics such as GKE enterprise cluster management and secure patching practices. These practices protect both applications and users while ensuring deployments remain resilient and up to date.

Enabling secure cloud development environments.

• 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)

Summary: To support developer productivity, this section explores best practices for bootstrapping secure development environments. Key tools include Cloud Workstations, Cloud Shell, and the customization of environments using IDEs, SDKs, and custom images. These ensure developers quickly gain access to the right configurations without compromising security.

Additionally, it introduces AI assistance tools like Cloud Code and Gemini Code Assist. These innovations enhance coding and operational workflows by suggesting intelligent solutions and guidance, enabling developers to work faster and smarter in the cloud.

Domain 2: Building and implementing CI/CD pipelines for applications and infrastructure (27% of the exam)

Designing and managing CI/CD pipelines.

• 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)

Summary: Here you will learn to build and maintain pipelines that serve both applications and infrastructure. Artifact Registry plays a major role in managing binaries and images, while deployment flexibility across serverless options, GKE clusters, and hybrid setups guarantees wide adaptability. Testing and approval processes ensure quality remains consistent across delivery flows.

The framework applies CI/CD not just to application code but also to infrastructure. This aligns pipelines with enterprise requirements for compliance and reliability, making them a central pillar of modern DevOps engineering.

Implementing CI/CD pipelines.

• 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

Summary: This section focuses on implementing pipelines in ways that ensure smooth and traceable deployments. You will become familiar with auditing and observing deployment progress using Artifact Registry, Cloud Deploy, and Cloud Audit Logs.

By applying deployment strategies such as rolling updates and canary releases, you can reduce risk while ensuring new versions reach production with confidence. Troubleshooting processes further reinforce your ability to resolve issues quickly, protecting system performance and user satisfaction.

Managing CI/CD configuration and secrets.

• Key management (e.g., Cloud Key Management Service)
• Secret management (e.g., Secret Manager, Certificate Manager)
• Build versus runtime secret injection

Summary: This section ensures you can manage secrets efficiently throughout the CI/CD lifecycle. Google Cloud services like Cloud Key Management and Secret Manager help you securely store and inject secrets where needed.

You will also compare build-time versus runtime secret injection approaches, ensuring the right strategy is chosen based on sensitivity and use case. This creates secure, scalable environments that safeguard credentials and system trust.

Securing the CI/CD deployment pipeline.

• 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

Summary: Security in CI/CD pipelines is not optional, and this section highlights how vulnerabilities are identified and mitigated. Using Artifact Registry vulnerability scanning and aligning with SLSA frameworks ensures that supply chains remain protected.

Integrating IAM policies based on environment further differentiates privileges for production versus test environments. Together, these practices defend your software delivery chain against unauthorized access and risk while ensuring compliance.

Domain 3: Applying site reliability engineering practices to applications (23% of the exam)

Balancing change, velocity, and reliability of the service.

• Defining SLIs (e.g., availability, latency), SLOs, and SLAs
• Error budgets
• Opportunity cost of risk and reliability (e.g., number of “nines”)

Summary: This section covers how to balance the speed of change with reliable operations. Understanding the core metrics of SLIs, SLOs, and SLAs helps you track system health, while error budgets enable teams to innovate responsibly without overstressing reliability boundaries.

With a focus on trade-offs, this introduces the idea of reliability as a business decision. By deciding how many "nines" of availability are appropriate, teams can align their operations with customer expectations and organizational priorities.

Managing service lifecycle.

• 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)

Summary: Managing services involves guiding them through stages from launch to retirement. This section covers step-by-step processes to introduce, evolve, and transition services using onboarding checklists and deployment plans.

Capacity planning ensures services are right-sized against quotas and limits, and autoscaling options are explored across Cloud Run, GKE, and instance groups. Together, these ensure that services adapt to user workloads without interruptions.

Mitigating incident impact on users.

• Draining/redirecting traffic
• Adding capacity
• Rollback strategies

Summary: Every system faces issues, and this section focuses on softening incident impacts. With draining and redirecting traffic, you ensure continuity during changes or failures. Adding capacity during demand spikes keeps services responsive.

Rollback mechanisms ensure rapid recovery when an update introduces disruptions. These practices build user trust by protecting service experience even when incidents occur.

Domain 4: Implementing observability practices (20% of the exam)

Managing logs.

• 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])

Summary: Effective observability begins with logs, and this section demonstrates how logs can be collected, optimized, exported, and retained. Cloud Logging agents and other native integrations make log ingestion straightforward, while export options like BigQuery and Pub/Sub expand analytical capabilities.

It also emphasizes cost-efficiency and handling sensitive data responsibly. This combination enables organizations to maximize value while ensuring compliance with privacy regulations.

Managing metrics.

• 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

Summary: This section explains how metrics provide health signals for applications and platforms. Google Cloud Managed Service for Prometheus and custom metric capabilities ensure comprehensive monitoring for diverse environments.

Metrics Explorer allows exploratory analysis, while synthetic monitoring highlights how artificial requests can uncover performance before users are affected. These capabilities shape proactive operations.

Managing dashboards and alerts.

• 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

Summary: Dashboards and alerts combine observability data into actionable insights. You will create customized views for different audiences while aligning with operational playbooks.

Alerting systems, including third-party integrations, provide timely notifications on defined thresholds. These promote faster incident detection and remediation, which drives resilience and efficiency.

Domain 5: Optimizing performance and troubleshooting (15% of the exam)

Troubleshooting issues.

• Infrastructure issues
• Application issues
• CI/CD pipeline issues
• Observability issues
• Performance and latency issues

Summary: This section addresses the ability to identify and resolve performance bottlenecks and operational problems. You’ll focus on diagnosing a wide variety of issues affecting infrastructure, applications, and pipelines.

By considering observability and performance factors like latency, you can resolve problems holistically. This equips you to sustain reliable delivery while maximizing system speed and responsiveness.

Implementing debugging tools in Google Cloud.

• Application instrumentation
• Cloud Trace
• Error Reporting

Summary: Debugging is covered here with practices and tools designed to enhance root cause analysis. Cloud Trace delivers request latency visualization, while Error Reporting aggregates and surfaces exceptions.

By leveraging instrumentation within applications, teams can further uncover unexpected behavior. Together, these tools foster proactive problem resolution and minimal disruption.

Optimizing resource utilization and costs.

• 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)

Summary: This section emphasizes cost optimization and efficient utilization of cloud resources. Practices involve using compute discounts, assessing network tiers, and employing spot VMs for cost-sensitive workloads.

Recommendations from Google Cloud intelligent recommenders further aid cost and performance planning. The goal is to achieve a cost-efficient yet secure and highly performant cloud infrastructure.