GCP Cloud DevOps Engineer Foundations

The Google Cloud DevOps Engineer exam validates skills for building reliable delivery pipelines and operating services. Cloud Build compiles source and creates container images, while Cloud Deploy automates delivery pipelines and progressive rollouts. Artifact Registry stores images and packages, and Cloud Source Repositories hosts private Git repositories. Infrastructure as code uses version-controlled configuration, and Deployment Manager provides declarative deployment. Cloud Build triggers start builds on repository events, and Config Sync keeps GKE clusters aligned with Git. Teams use Dockerfiles and image registries to standardize builds. A strong pipeline includes automated tests, artifact promotion, rollback, and approval gates so changes reach production safely.

Site reliability engineering applies software engineering to operations. A service level objective defines a reliability target, a service level indicator is a measured metric, and an error budget is the allowed failure derived from the SLO. An SLA is a contract that commits to service levels. Release strategies reduce risk: canary releases send small traffic shares to new versions, rolling deployments update instances gradually, and blue/green deployments switch between two environments. Load tests verify behavior under expected demand. Teams use error budgets to decide whether to release or focus on reliability work. Reliability also requires redundancy, health checks, backups, and incident response procedures.

Observability combines metrics, logs, and traces. Cloud Monitoring collects metrics, builds dashboards, and evaluates alerting policies. Cloud Logging stores and queries logs, and log-based metrics derive metrics from log patterns. Cloud Trace analyzes request latency, Cloud Profiler samples CPU and memory, and Cloud Error Reporting groups application errors. Uptime checks verify endpoint availability from multiple locations. Alerting policies notify teams when thresholds are crossed. A mature observability strategy defines golden signals such as latency, traffic, errors, and saturation. Teams use dashboards and runbooks to respond quickly, and correlate metrics, logs, and traces during incidents.

Secure DevOps combines identity, artifact security, and controlled deployment. Cloud IAM grants least-privilege roles, service accounts identify workloads, and Secret Manager stores sensitive values. Cloud KMS manages encryption keys, Cloud Armor protects web apps, and Artifact Analysis scans container images for vulnerabilities. Binary Authorization requires signed images before deployment, and Cloud Audit Logs records actions. Deployment strategies include progressive delivery, feature flags, rollback, and GitOps. Pipelines promote artifacts through environments with approval gates. Teams should test rollback procedures, verify image signatures, and enforce separation of duties so changes are safe and auditable.

DevOps teams must balance reliability with cost. Cloud Billing tracks spend and budgets, and budget alerts notify teams when thresholds are crossed. Recommender provides recommendations for rightsizing, idle resources, and cost optimization. Quotas limit resource usage, and committed use discounts reduce cost for predictable workloads. Cloud Monitoring provides utilization data that supports decisions. Operations practices include infrastructure as code, runbooks, incident management, and continuous improvement. Teams use error budgets, SLOs, and dashboards to prioritize work. Regular reviews identify waste, unused resources, and opportunities to consolidate workloads. A mature team automates remediation and documents every operational procedure.