Google Cloud Certified Professional Cloud DevOps Engineer

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)