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Google Cloud Professional Cloud DevOps Engineer Syllabus

Bootstrapping and maintaining a Google Cloud organization

Designing the resource hierarchy, managing infrastructure as code, CI/CD architecture, environments and secure development environments.

20%

Weight

Questions

Marks

Resource Hierarchy and Shared Services

Folders, projects and application-centric grouping
Shared VPC, VPC Network Peering and Private Service Connect
IAM, organization policies and service accounts
Multi-project logging and monitoring
Data residency

Infrastructure as Code and CI/CD Architecture

Terraform, Infrastructure Manager and Config Connector
Cloud Foundation Toolkit and Helm
Cloud Build, Cloud Deploy, Skaffold and Kustomize
Third-party tools: Jenkins, Argo CD, Packer, kpt

Environments and Developer Tooling

Ephemeral and multiple environments
GKE fleets and configuration management
Safe patching and upgrades
Cloud Workstations and Cloud Shell
Gemini Code Assist, Gemini Cloud Assist and Gemini CLI

Building and implementing CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads

Designing, implementing, securing and managing configuration and secrets for deployment pipelines.

25%

Weight

Questions

Marks

Designing and Implementing Pipelines

Pipeline triggers and approval flows
Canary, blue/green and rolling deployments
Traffic splitting and feature flags
Deployment auditing with Cloud Audit Logs
Troubleshooting pipelines

Configuration and Secrets

Secret Manager and Cloud KMS
Certificate Manager and Parameter Manager
Workload Identity Federation
Build-time vs. runtime secret injection

Securing the Pipeline

Artifact Analysis vulnerability scanning
Binary Authorization
SLSA supply chain levels
Environment-based IAM policies

Applying site reliability engineering practices

Balancing reliability and velocity, managing the service lifecycle and mitigating incident impact.

18%

Weight

Questions

Marks

Reliability Targets

SLIs, SLOs and SLAs
Error budgets
Cost of reliability and 'nines'
Balancing change velocity with reliability

Service Lifecycle and Incidents

Capacity planning with quotas and reservations
Autoscaling with MIGs, Cloud Run and GKE
Draining or redirecting traffic
Rollback strategies
Adding capacity during incidents

Implementing observability practices and troubleshooting issues

Collecting telemetry, analyzing logs, metrics and traces, alerting and troubleshooting.

25%

Weight

Questions

Marks

Telemetry Collection

Ops Agent and OpenTelemetry
Managed Service for Prometheus
Custom and log-based metrics
Synthetic monitors
Log volume and cost optimization

Logs, Metrics, Dashboards and Alerts

Logs Explorer and Logging query language
Log export to BigQuery, Pub/Sub and Cloud Storage
Metrics Explorer and PromQL dashboards
SLO-based alerting policies
PagerDuty and webhook integrations

Tracing and Troubleshooting

Distributed traces, spans and waterfalls
Correlating trace IDs with structured logs
Troubleshooting infrastructure and CI/CD issues
Diagnosing application performance and latency

Optimizing performance and cost

Collecting performance data and applying FinOps practices to reduce resource and observability costs.

12%

Weight

Questions

Marks

Performance Information

Application performance monitoring
Active Assist insights
Google Cloud recommenders
Resource efficiency analysis

FinOps Practices

Spot VMs
Committed-use and sustained-use discounts
Network service tiers
Observability cost control
Cost optimization for GKE, Cloud Run and Compute Engine

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Learning Progress

Bootstrapping and maintaining a Google Cloud organization 85%

Building and implementing CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads 92%

Practice Test Scores

95%

Latest Score

Above passing threshold

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