CNPA Exam Dumps - Linux Foundation Kubernetes and Cloud Native Questions and Answers

Traces provide end-to-end visibility into how a request flows through multiple services in a distributed system. Option A is correct because tracing captures spans (individual service operations) and stitches them together to form a complete picture of request execution, including latency, bottlenecks, and dependencies.

Option B (logs) provide detailed event records but lack contextual linkage across services. Option C (events) are discrete system occurrences, not correlated request flows. Option D (metrics) provide aggregated numerical data like latency or throughput but cannot show request-level detail across distributed systems.

Tracing is especially critical in microservices architectures where a single request may traverse dozens of services. Tools like OpenTelemetry, Jaeger, and Zipkin are commonly used to implement distributed tracing, which is essential for debugging, performance optimization, and improving reliability.

Developer portals like Backstage act as the single entry point for platform services, APIs, golden paths, and documentation. Option A is correct because centralizing access greatly reduces the friction developers face when trying to adopt a new platform. Instead of searching across fragmented systems or learning low-level Kubernetes details, developers can find everything in one place, including templates, service catalogs, automated workflows, and governance policies.

Option B is irrelevant to platform adoption. Option C may foster community sharing but does not directly address adoption challenges. Option D contradicts platform engineering principles, which emphasize democratizing access and self-service rather than restricting tools to senior developers.

By providing a unified experience, portals improve discoverability, consistency, and self-service. They reduce cognitive load and support the platform engineering principle of improving developer experience, making adoption of new platforms smoother and more efficient.

In Istio, PeerAuthentication is used to configure how workloads authenticate traffic coming from other services in the mesh. Option C is correct because PeerAuthentication primarily secures service-to-service communication using mutual TLS (mTLS), ensuring encryption in transit and verifying the identity of both communicating parties.

Option A (network policies for ingress traffic) relates to Kubernetes NetworkPolicy, not Istio PeerAuthentication. Option B (traffic routing) is handled by Istio’s VirtualService and DestinationRule resources. Option D (monitoring/logging) is part of Istio’s telemetry features, not PeerAuthentication.

PeerAuthentication policies define whether mTLS is disabled, permissive, or strict, giving platform teams fine-grained control over how services communicate securely. This aligns with zero-trust security models and ensures compliance with organizational policies without requiring application code changes.

Mutual TLS (mTLS) is a core feature of service meshes, such as Istio or Linkerd, that enhances security in cloud native environments by ensuring that both communicating services authenticate each other and that the communication channel is encrypted. Option A is correct because mTLS delivers two critical benefits: authentication (verifying the identity of both client and server services) and encryption (protecting data in transit from interception or tampering).

Option B is incorrect because mTLS does not bypass security—it enforces it. Option C is partly true in that service meshes often support observability and logging, but that is not the primary purpose of mTLS. Option D relates to scaling, which is outside the scope of mTLS.

In platform engineering, mTLS is a fundamental security mechanism that provides zero-trust networking between microservices, ensuring secure communication without requiring application-level changes. It strengthens compliance with security and data protection requirements, which are crucial in regulated industries.

The Kubernetes reconciliation process ensures that the actual cluster state matches the desired state defined in manifests. The Kubernetes Controller (option D) is responsible for watching the state of resources through the API Server and taking action to reconcile differences. For example, the Deployment Controller ensures that the number of Pods matches the replica count specified, while the Node Controller monitors node health.

Option A (Scheduler) is incorrect because the Scheduler’s role is to assign Pods to nodes based on constraints and availability, not ongoing reconciliation. Option B (Dashboard) is simply a UI for visualization and does not manage cluster state. Option C (API Server) exposes the Kubernetes API and serves as the communication hub, but it does not perform reconciliation logic itself.

Controllers embody the core Kubernetes design principle: continuous reconciliation between declared state and observed state. This makes them fundamental to declarative infrastructure and aligns with GitOps practices where controllers continuously enforce desired configurations from source control.