Kubernetes and Cloud Native CNPA Updated Exam

The GitOps model is built on the principle that the desired state of infrastructure and applications must be stored in Git as the single source of truth. Option D is correct because Git provides versioning, immutability, and auditability, while reconciliation controllers (e.g., Argo CD or Flux) pull the desired state into the system continuously. This ensures that actual cluster state always matches the declared Git state.

Option A is partially correct but fails because GitOps eliminates manual push workflows—automation ensures changes are pulled and reconciled. Option B describes Kubernetes CRDs, which may be part of the system but do not embody GitOps on their own. Option C contradicts GitOps principles, which rely on pull-based reconciliation, not centralized push.

Storing desired state in Git provides full traceability, automated rollbacks, and continuous reconciliation, improving reliability and compliance. This makes GitOps a core practice for cloud native platform engineering.

GitOps uses a pull-based approach, where controllers inside the cluster continuously reconcile the desired state stored in Git with the actual cluster state. Option A is correct because GitOps sync agents (e.g., Argo CD, Flux) poll or watch Git repositories for changes and automatically apply updates to the cluster.

Option B reverses the model—clusters do not send updates to Git; Git is the source of truth. Option C is partially misleading: webhooks can trigger faster syncs but reconciliation is still pull-based. Option D misrepresents GitOps—Git never pushes directly to clusters.

This pull-based approach ensures greater security (clusters pull changes rather than exposing themselves to pushes), consistency (Git as source of truth), and continuous reconciliation (drift correction).

Lead Time for Changes is the DORA metric that best measures the efficiency and impact of platform initiatives. Option A is correct because it tracks the time from code commit to successful production deployment, directly reflecting how effectively a platform enables developers to deliver software.

Option B (MTTR) measures resilience and recovery speed, not efficiency. Option C (Change Failure Rate) measures deployment stability, while Option D (SLAs) are contractual agreements, not engineering performance metrics.

By reducing lead time, platform engineering demonstrates its ability to provide self-service, automation, and streamlined CI/CD workflows. This makes Lead Time for Changes a critical measurement of platform efficiency and developer experience improvements.

The most effective way to achieve consistent, automated deployments across multiple Kubernetes clusters is to combine GitOps controllers (e.g., Argo CD, Flux) with declarative configurations managed by Helm and Crossplane. Option A is correct because the GitOps controller continuously reconciles the desired state stored in Git—Helm charts for applications and Crossplane manifests for infrastructure—ensuring consistency across clusters.

Option B and D rely on manual updates, which are error-prone and not scalable. Option C mischaracterizes GitOps by suggesting push-based pipelines rather than the core GitOps model of pull-based reconciliation.

This combination leverages Helm for application packaging, Crossplane for cloud infrastructure provisioning, and GitOps for declarative, version-controlled delivery. It ensures applications remain in sync with Git, providing auditability, automation, and resilience in multi-cluster environments.