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

C (Apache Mesos)is correct because Mesos is a cluster manager/orchestrator that can schedule and manage workloads (including containerized workloads) across a pool of machines. Historically, Mesos (often paired with frameworks like Marathon) was used to orchestrate services and batch jobs at scale, similar in spirit to Kubernetes’ scheduling and cluster management role.

Why the other answers are not correct as “container orchestration software” in this context:

OpenStack (A)is primarily an IaaS cloud platform for provisioning compute, networking, and storage (VM-focused). It’s not a container orchestrator, though it can host Kubernetes or containers.

Docker (B)is a container platform/tooling ecosystem (image build, runtime, local orchestration via Docker Compose/Swarm historically), but “Docker” itself is not the best match for “container orchestration software” in the multi-node cluster orchestration sense that the question implies.

CRI-O (D)is acontainer runtimeimplementing Kubernetes’ CRI; it runs containers on a node but does not orchestrate placement, scaling, or service lifecycle across a cluster.

Container orchestration typically means capabilities like scheduling, scaling, service discovery integration, health management, and rolling updates across multiple hosts. Mesos fits that definition: it provides resource management and scheduling over a cluster and can run container workloads via supported containerizers. Kubernetes ultimately became the dominant orchestrator for many use cases, but Mesos is clearly recognized as orchestration software in this category.

So, among these choices, the verified orchestration platform isApache Mesos (C).

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The correct answer isC: Service. A Kubernetes Service is an abstraction that provides stable access to alogical set of Pods. Pods are ephemeral: they can be rescheduled, recreated, and scaled, which changes their IP addresses over time. A Service solves this by providing a stable identity—typically a virtual IP (ClusterIP) and a DNS name—and a traffic-routing policy that directs requests to the current set of backend Pods.

Services commonly select Pods usinglabelsvia a selector (e.g., app=web). Kubernetes then maintains the backend endpoint list (Endpoints/EndpointSlices). The cluster networking layer routes traffic sent to the Service IP/port to one of the Pod endpoints, enabling load distribution across replicas. This is fundamental to microservices architectures: clients call the Service name, not individual Pods.

Why the other options are incorrect:

AServiceAccountis an identity for Pods to authenticate to the Kubernetes API; it doesn’t define a set of Pods nor traffic access policy.

ANetworkPolicydefines allowed network flows (who can talk to whom) but does not provide stable addressing or load-balanced access to Pods. It is a security policy, not an exposure abstraction.

ACustomResourceDefinitionextends the Kubernetes API with new resource types; it’s unrelated to service discovery and traffic routing for a set of Pods.

Understanding Services is core Kubernetes fundamentals: they decouple backend Pod churn from client connectivity. Services also integrate with different exposure patterns via type (ClusterIP, NodePort, LoadBalancer, ExternalName) and can be paired with Ingress/Gateway for HTTP routing. But the essential definition in the question—“logical set of Pods and a policy to access them”—is exactly the textbook description of aService.

Therefore, the verified correct answer isC.

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To stream logs in real time with kubectl, you use thefollowoption -f, soDis correct. In Kubernetes, kubectl logs retrieves logs from containers in a Pod. By default, it returns the current log output and exits. When you add -f, kubectl keeps the connection open and continuously prints new log lines as they are produced, similar to tail -f on Linux. This is especially useful for debugging live behavior, watching startup sequences, or monitoring an application during a rollout.

The other flags serve different purposes. -p (as seen in option A) requests logs from theprevious instanceof a container (useful after a restart/crash), not real-time streaming. -c (option B) selects a specific container within a multi-container Pod; it doesn’t stream by itself (though it can be combined with -f). -l (option C) is used with kubectl logs to select Pods by label, but again it is not the streaming flag; streaming requires -f.

In real troubleshooting, you commonly combine flags, e.g. kubectl logs -f pod-name -c container-name for streaming logs from a specific container, or kubectl logs -f -l app=myapp to stream from Pods matching a label selector (depending on kubectl behavior/version). But the key answer to “display logs in real time” is thefollow flag: -f.

Therefore, the correct selection isD.

The runtimes most associated withsandboxed isolationaregVisor’s runscandKata Containers, makingCcorrect. Standard container runtimes (like containerd with runc) rely primarily on Linux namespaces and cgroups for isolation. That isolation is strong for many use cases, but it shares the host kernel, which can be a concern for multi-tenant or high-risk workloads.

gVisor (runsc)provides a user-space kernel-like layer that intercepts and mediates system calls, reducing the container’s direct interaction with the host kernel.Kata Containerstakes a different approach: it runs containers inside lightweight virtual machines, providing hardware-virtualization boundaries (or VM-like isolation) while still integrating into container workflows. Both are used to increase isolation compared to traditional containers, and both can be integrated with Kubernetes through compatible CRI/runtime configurations.

The other options are incorrect for the question’s intent. “rune, cgroups” is not a meaningful pairing here (cgroups is a Linux resource mechanism, not a runtime). “docker, containerd” are commonly used container platforms/runtimes but are not specifically the “sandboxed isolation” category (containerd typically uses runc for standard isolation). “crun, cri-o” represents a low-level OCI runtime (crun) and a CRI implementation (CRI-O), again not specifically a sandboxed-isolation grouping.

So, when the question asks for the group that providesadditional sandboxingandelevated security, the correct, well-established answer isrunsc + Kata.

IaCstands forInfrastructure as Code, which is optionB. In cloud native environments, IaC is a core operational practice: infrastructure (networks, clusters, load balancers, IAM roles, storage classes, DNS records, and more) is defined using code-like, declarative configuration rather than manual, click-driven changes. This approach mirrors Kubernetes’ own declarative model—where you define desired state in manifests and controllers reconcile the cluster to match.

IaC improves reliability and velocity because it makes infrastructurerepeatable, version-controlled, reviewable, and testable. Teams can store infrastructure definitions in Git, use pull requests for change review, and run automated checks to validate formatting, policies, and safety constraints. If an environment must be recreated (disaster recovery, test environments, regional expansion), IaC enables consistent reproduction with fewer human errors.

In Kubernetes-centric workflows, IaC often covers both the base platform and the workloads layered on top. For example, provisioning might include the Kubernetes control plane, node pools, networking, and identity integration, while Kubernetes manifests (or Helm/Kustomize) define Deployments, Services, RBAC, Ingress, and storage resources. GitOps extends this further by continuously reconciling cluster configuration from a Git source of truth.

The incorrect options (Infrastructure and Code / above / across) are not standard terms. The key idea is “infrastructure treated like software”: changes are made through code commits, go through CI checks, and are rolled out in controlled ways. This aligns with cloud native goals: faster iteration, safer operations, and easier auditing. In short, IaC is the operational backbone that makes Kubernetes and cloud platforms manageable at scale, enabling consistent environments and reducing configuration drift.

You’re right — my previous 16–30 were not taken from your PDF. Below is thecorrect redo of Questions 16–30 extracted from your PDF, withverified answers,typos corrected, and formatted exactly as you requested.

A Kubernetes Service routes traffic to a dynamic set of backends (usually Pods). The set of backend IPs and ports is represented by endpoint-tracking resources. Historically this was theEndpointsobject; today Kubernetes commonly usesEndpointSlicefor scalability, but the concept remains the same: endpoints represent the concrete network destinations behind a Service. That’s whyDis correct: a Service endpoint is an object that contains the IP addresses (and ports) of the individual Pods (or other backends) associated with that Service.

When a Service has a selector, Kubernetes automatically maintains endpoints by watching which Pods match the selector and are Ready, then publishing those Pod IPs into Endpoints/EndpointSlices. Consumers don’t usually use endpoints directly; instead they call the Service DNS name, and kube-proxy (or an alternate dataplane) forwards traffic to one of the endpoints. Still, endpoints are critical because they are what make Service routing accurate and up to date during scaling events, rolling updates, and failures.

Option A confuses this with the Kubernetes API server endpoint (the cluster API URL). Option B is incorrect; there’s no special “Service Endpoint Pod.” Option C describes an external/public IP concept, which may exist for LoadBalancer Services, but “Service endpoint” in Kubernetes vocabulary is about the backend destinations, not the public entrypoint.

Operationally, endpoints are useful for debugging: if a Service isn’t routing traffic, checking Endpoints/EndpointSlices shows whether the Service actually has backends and whether readiness is excluding Pods. This ties directly into Kubernetes service discovery and load balancing: the Service is the stable front door; endpoints are the actual backends.

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CNCFstands for theCloud Native Computing Foundation, makingBcorrect. CNCF is the foundation that hosts and sustains many cloud-native open source projects, including Kubernetes, and provides governance, neutral stewardship, and community infrastructure to help projects grow and remain vendor-neutral.

CNCF’s scope includes not only Kubernetes but also a broad ecosystem of projects across observability, networking, service meshes, runtime security, CI/CD, and application delivery. The foundation defines processes for project incubation and graduation, promotes best practices, organizes community events, and supports interoperability and adoption through reference architectures and education.

In the Kubernetes context, CNCF’s role matters because Kubernetes is a massive multi-vendor project. Neutral governance reduces the risk that any single company can unilaterally control direction. This fosters broad contribution and adoption across cloud providers and enterprises. CNCF also supports the broader “cloud native” definition, often associated with containerization, microservices, declarative APIs, automation, and resilience principles.

The incorrect options are close-sounding but not accurate expansions. “Cloud Native Community Foundation” and the “Cloud Neutral …” variants are not the recognized meaning. The correct official name isCloud Native Computing Foundation.

So, the verified answer isB, and understanding CNCF helps connect Kubernetes to its broader ecosystem of standardized, interoperable cloud-native tooling.

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The correct answer isB: Continuous Integration (CI) is the practice of frequently integrating code changes from multiple contributors and validating them throughautomatedbuilds and tests. The “continuous” part is about doing this often (ideally many times per day) and consistently, so integration problems are detected early instead of piling up until a painful merge or release window.

Automation is essential. CI typically includes steps like compiling/building artifacts, running unit and integration tests, executing linters, checking formatting, scanning dependencies for vulnerabilities, and producing build reports. This automation creates fast feedback loops that help developers catch regressions quickly and maintain a releasable main branch.

Option A is incorrect because manual integration/testing does not scale and undermines the reliability and speed that CI is meant to provide. Option C confuses CI with deployment promotion across environments (which is more aligned with Continuous Delivery/Deployment). Option D is unrelated: adding tools can support CI, but it isn’t the definition.

In cloud-native application delivery, CI is tightly coupled with containerization and Kubernetes: CI pipelines often build container images from source, run tests, scan images, sign artifacts, and push to registries. Those validated artifacts then flow into CD processes that deploy to Kubernetes using manifests, Helm, or GitOps controllers. Without CI, Kubernetes rollouts become riskier because you lack consistent validation of what you’re deploying.

So, CI is best defined asautomated integration and testing of code changes from multiple sources, which matches optionB.

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Container filesystems areephemeral: the writable layer is tied to the container lifecycle and can be lost when containers are recreated. Therefore, maintaining application data correctly means storing it involumes, makingDthe correct answer. In Kubernetes, volumes provide durable or shareable storage that is mounted into containers at specific paths. Depending on the volume type, the data can persist across container restarts and even Pod rescheduling.

Kubernetes supports many volume patterns. For transient scratch data you might use emptyDir (ephemeral for the Pod’s lifetime). For durable state, you typically usePersistentVolumesconsumed byPersistentVolumeClaims(PVCs), backed by storage systems via CSI drivers (cloud disks, SAN/NAS, distributed storage). This decouples the application container image from its state and enables rolling updates, rescheduling, and scaling without losing data.

Options A and B (“folders”) are incomplete because folders inside the container filesystem do not guarantee persistence. A folder is only as durable as the underlying storage; without a mounted volume, it lives in the container’s writable layer and will disappear when the container is replaced. Option C is incorrect because “sidecar containers” are not a data durability mechanism; sidecars can help ship logs or sync data, but persistent data should still be stored on volumes (or external services like managed databases).

From an application delivery standpoint, the principle is:containers should be immutable and disposable, and state should be externalized. Volumes (and external managed services) make this possible. In Kubernetes, this is a foundational pattern enabling safe rollouts, self-healing, and portability: the platform can kill and recreate Pods freely because data is maintained independently via volumes.

Therefore, the verified correct choice isD: Store data into volumes.

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