Free Practice Questions for Google Professional Cloud DevOps Engineer Certification

Considerations include:

- Organizing resources (e.g., application-centric, projects, folders) - Shared networking (e.g., Shared VPC, VPC Network Peering, Private Service Connect) - Multi-project monitoring and logging - Identity and Access Management (IAM) roles and organization-level policies - Creating and managing service accounts - Data residency

Considerations include:

- Infrastructure-as-code tooling and managed services (e.g., Infrastructure Manager, Cloud Foundation Toolkit, Config Connector, GitOps, Terraform, Helm) - Making infrastructure changes using Google-recommended practices and blueprints - Automation with scripting (e.g., Python, Go)

Considerations include:

- Continuous integration (CI) with Cloud Build - Continuous delivery (CD) with Cloud Deploy, including Kustomize and Skaffold - Artifact Registry configuration - Widely used third-party tooling (e.g., Git, Jenkins, Argo CD, Packer, kpt) - Security of CI/CD tooling

Considerations include:

- Managing ephemeral environments - Managing configuration and policy - Managing Google Kubernetes Engine (GKE) clusters across an enterprise (e.g., fleets) - Safe and secure patching and upgrading practices

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., Gemini Code Assist, Gemini Cloud Assist, Gemini CLI)

Considerations include:

- CI/CD of applications and infrastructure - Artifact management with Artifact Registry - Deployment to hybrid and multi-cloud environments (e.g., GKE) - CI/CD pipeline triggers - Configuring deployment processes (e.g., approval flows)

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, feature flags) and defining success metrics based on application or ML pipeline telemetry - Troubleshooting and mitigating deployment issues

Considerations include:

- Key management (e.g., Cloud Key Management Service) - Configuration and secret management (e.g., Secret Manager, Certificate Manager, Parameter Manager, Workload Identity Federation) - Build versus runtime secret injection

Considerations include:

- Artifact Analysis and vulnerability scanning - Software supply chain security (e.g., Binary Authorization, Supply-chain Levels for Software Artifacts [SLSA] framework) - IAM policies based on environment

Considerations include:

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

Considerations include:

- Service management (e.g., planning, deployment, maintenance, retirement) - Capacity planning (e.g., quotas, limits, reservations, Dynamic Workload Scheduler) - Autoscaling (e.g., managed instance groups, Cloud Run, GKE)

Considerations include:

- Draining/redirecting traffic - Adding capacity - Rollback strategies

Considerations include:

- Collecting and importing logs (e.g., Ops Agent, OpenTelemetry, Cloud Audit Logs, VPC Flow Logs, Cloud Service Mesh) - Optimizing logs (e.g., filtering, sampling, exclusions, cost management, source considerations) - Collecting metrics (e.g., from applications, platforms, networking, Cloud Service Mesh, Google Cloud Managed Service for Prometheus, hybrid/multi-cloud environments) - Creating synthetic monitors to proactively probe application endpoints and workflows - Creating custom metrics, including log-based metrics

Considerations include:

- Analyzing logs using the Logs Explorer and the Logging query language - Exporting and retaining logs (e.g., routing to BigQuery, Pub/Sub, Cloud Storage) - Handling sensitive data (e.g., using log processors to redact personally identifiable information [PII], protected health information [PHI]) - Using Gemini Cloud Assist for AI-powered log analysis

Considerations include:

- Analyzing metrics using the Metrics Explorer - Managing dashboards (e.g., creating, filtering, sharing, playbooks, PromQL) - Configuring alerting and alerting policies (e.g., SLIs, SLOs, cost control) - Integrating with third-party alerting tools (e.g., webhooks, PagerDuty, Rootly) - Leveraging Gemini Cloud Assist for metrics interpretation

Considerations include:

- Utilizing tracing frameworks (e.g., OpenTelemetry) - Analyzing trace waterfalls and spans - Correlating trace IDs with structured logs - Employing Gemini Cloud Assist for trace analysis

Considerations include:

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

Considerations include:

- Application performance monitoring - Active Assist insights and recommendations

Considerations include:

- Observability costs - Spot virtual machines (VMs) - Optimizing resource usage for cost and efficiency - Infrastructure cost planning (e.g., committed-use discounts, sustained-use discounts, network tiers) - Leveraging Google Cloud recommenders (e.g., cost, security, performance, manageability, reliability) - Optimizing individual workload costs (e.g., GKE, Cloud Run, Compute Engine)