CNPA Dumps With Exact Questions and Answers

A key advantage of using a service mesh is its ability to secure service-to-service communication transparently, without requiring application code changes. Option A is correct because service meshes (e.g., Istio, Linkerd) provide mutual TLS (mTLS) by default, ensuring both encryption in transit and authentication between services. This establishes a zero-trust networking model inside the cluster.

Option B (scaling) is managed by Kubernetes (Horizontal Pod Autoscaler), not service mesh. Option C (logging) may be supported as an observability feature, but it is not the primary security benefit. Option D (IP allowlisting) is an outdated, less flexible mechanism compared to identity-based policies that meshes provide.

Service meshes enforce security consistently across all services, support fine-grained policies, and ensure compliance without burdening developers with complex configurations. This makes mTLS a foundational benefit in cloud native platform security.

Automation in DevOps practices is central to platform engineering because it enables faster, reliable, and repeatable deployments. Option D is correct: automation accelerates deployments, reduces bottlenecks, and enables continuous delivery and rapid iterations. By automating build, test, and deployment pipelines, teams can deliver new features quickly while maintaining high quality and compliance.

Option A is incorrect because automation does not reduce the need for communication—it complements collaboration by removing friction. Option B is unrealistic: some manual oversight may remain (e.g., in production approvals for sensitive workloads). Option C is not a primary benefit—while tools may be involved, the focus is on outcomes, not tool dependency.

By embedding automation, teams reduce toil, enforce consistency, and free developers to focus on value creation rather than repetitive tasks. This results in shorter lead times, higher deployment frequency, and overall improved developer experience, which aligns with DORA metrics.

The fundamental difference between a traditional CI/CD model and a GitOps model lies in how changes are applied to the Kubernetes cluster—whether they are "pushed" to the cluster by an external system or "pulled" by an agent running inside the cluster.

CI/CD (Push Model)In a typical CI/CD pipeline for Kubernetes, the CI/CD server (like Jenkins, GitLab CI, or GitHub Actions) is granted credentials to access the cluster. When a pipeline runs, it executes commands like kubectl apply or helm upgrade to push the new application configuration and image versions directly to the Kubernetes API server.

Actor: The CI/CD pipeline is the active agent initiating the change.

Direction: Changes flow from the CI/CD system to the cluster.

Security: Requires giving cluster credentials to an external system.

In a GitOps model, a Git repository is the single source of truth for the desired state of the application. An agent or controller (like Argo CD or Flux) runs inside the Kubernetes cluster. This controller continuously monitors the Git repository.

When it detects a difference between the desired state defined in Git and the actual state of the cluster, it pulls the changes from the repository and applies them to the cluster to bring it into the desired state. This process is called reconciliation.

Actor: The in-cluster controller is the active agent initiating the change.

Direction: The cluster pulls its desired state from the Git repository.

Security: The cluster's credentials never leave its boundary. The controller only needs read-access to the Git repository.