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

ADaemonSetensures that a copy of a Pod runs oneach node(or a selected subset of nodes), which matches optionAand makes it correct. DaemonSets are ideal for node-level agents that should exist everywhere, such as log shippers, monitoring agents, CNI components, storage daemons, and security scanners.

DaemonSets differ from Deployments/ReplicaSets because their goal is not “N replicas anywhere,” but “one replica per node” (subject to node selection). When nodes are added to the cluster, the DaemonSet controller automatically schedules the DaemonSet Pod onto the new nodes. When nodes are removed, the Pods associated with those nodes are cleaned up. You can restrict placement using node selectors, affinity rules, or tolerations so that only certain nodes run the DaemonSet (for example, only Linux nodes, only GPU nodes, or only nodes with a dedicated label).

Option B sounds like a ReplicaSet/Deployment behavior (stable set of replicas), not a DaemonSet. Option C describes CronJobs (scheduled, recurring run-to-completion workloads). Option D describes StatefulSets, which provide stable identity, ordering, and uniqueness guarantees for stateful replicas.

Operationally, DaemonSets matter because they often run critical cluster services. During maintenance and upgrades, DaemonSet update strategy determines how those node agents roll out across the fleet. Since DaemonSets can tolerate taints (like master/control-plane node taints), they can also be used to ensure essential agents run across all nodes, including special pools. Thus, the correct definition isA.

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A KubernetesService without selectorsrequires you to manage its backend endpoints manually, soBis correct. Normally, a Service uses aselectorto match a set of Pods (by labels). Kubernetes then automatically maintains the backend list (historically Endpoints, now commonly EndpointSlice) by tracking which Pods match the selector and are Ready. This automation is one of the key reasons Services provide stable connectivity to dynamic Pods.

When you create a Servicewithout a selector, Kubernetes has no way to know which Pods (or external IPs) should receive traffic. In that pattern, you explicitly create anEndpointsobject (or EndpointSlices, depending on your approach and controller support) that maps the Service name to one or more IP:port tuples. This is commonly used to representexternal services(e.g., a database running outside the cluster) while still providing a stable Kubernetes Service DNS name for in-cluster clients. Another use case is advanced migration scenarios where endpoints are controlled by custom controllers rather than label selection.

Why the other options are wrong: ServicetypeslikeClusterIP,NodePort, andLoadBalancerdescribe how a Service is exposed, but they do not inherently require manual endpoint management. A ClusterIP Servicewith selectors(D) is the standard case where endpoints are automatically created and updated. NodePort and LoadBalancer Services also typically use selectors and therefore inherit automatic endpoint management; the difference is in how traffic enters the cluster, not how backends are discovered.

Operationally, when using Services without selectors, you must ensure endpoint IPs remain correct, health is accounted for (often via external tooling), and you update endpoints when backends change. The key concept is:no selector → Kubernetes can’t auto-populate endpoints → you must provide them.

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In Kubernetes, aDeploymentis a higher-level controller that managesReplicaSets, and ReplicaSets in turn managePods. That is exactly what optionAstates, making it the correct answer.

A ReplicaSet’s job is straightforward: ensure that a specified number of Pod replicas matching a selector are running. It continuously reconciles actual state to desired state by creating new Pods when replicas are missing or removing Pods when there are too many. However, ReplicaSets alone do not provide the richer application rollout lifecycle features most teams need.

A Deployment adds those features by managing ReplicaSets across versions of your Pod template. When you update a Deployment (for example, change the container image tag), Kubernetes creates a new ReplicaSet with the new Pod template and then gradually scales the new ReplicaSet up and the old one down according to the Deployment strategy (RollingUpdate by default). Deployments also maintain rollout history, support rollback (kubectl rollout undo), and allow pause/resume of rollouts. This is why the common guidance is: you almost always create Deployments rather than ReplicaSets directly for stateless apps.

Option B is incorrect because stateful workloads are typically handled by StatefulSets, not ReplicaSets. Deployments can run stateless apps, but ReplicaSets are also used under Deployments and are not “for stateful only.” Option C is reversed: ReplicaSets are not “instances” of Deployments; Deployments create/manage ReplicaSets. Option D is incorrect because Jobs/CronJobs are separate controllers for run-to-completion workloads and do not define ReplicaSets as subsets.

So the accurate relationship is:Deployment → manages ReplicaSets → which manage Pods, enabling declarative updates and controlled rollouts.

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Ahybrid cloudarchitecture combinespublic cloudandprivate/on-premisesenvironments, often spanning multiple infrastructure domains while maintaining some level of portability, connectivity, and unified operations. OptionCcaptures the commonly accepted definition: services run acrosspublic and private clouds, includingon-premises data centers, soCis correct.

Hybrid cloud is not limited to a single cloud provider (which is why A is too restrictive). Many organizations adopt hybrid cloud to meet regulatory requirements, data residency constraints, latency needs, or to preserve existing investments while still using public cloud elasticity. In Kubernetes terms, hybrid strategies often include running clusters both on-prem and in one or more public clouds, then standardizing deployment through Kubernetes APIs, GitOps, and consistent security/observability practices.

Option B is incorrect because excluding data centers in different availability zones is not a defining property; in fact, hybrid deployments commonly use multiple zones/regions for resilience. Option D is a distraction: serverless inclusion or exclusion does not define hybrid cloud. Hybrid is about the combination ofinfrastructure environments, not a specific compute model.

A practical cloud-native view is that hybrid architectures introduce challenges around identity, networking, policy enforcement, and consistent observability across environments. Kubernetes helps because it provides a consistent control plane API and workload model regardless of where it runs. Tools like service meshes, federated identity, and unified monitoring can further reduce fragmentation.

So, the most accurate definition in the given choices isC: hybrid cloud combines public and private clouds, including on-premises infrastructure, to run services in a coordinated architecture.

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When you create a Service of typeLoadBalancerin a cloud environment, Kubernetes relies on cloud-provider integration to provision an external load balancer and allocate a public IP (or equivalent). The control plane component responsible for this integration is thecloud-controller-manager, soAis correct.

In Kubernetes, a LoadBalancer Service triggers a controller loop that calls the cloud provider APIs to create/update a load balancer that forwards traffic to the cluster (often via NodePorts on worker nodes, or via provider-specific mechanisms). The Service remains with EXTERNAL-IP: Pending until the cloud provider resource is successfully created and the controller updates the Service status with the assigned external address. If that status never updates, it usually indicates the cloud integration path is broken—commonly due to: missing cloud provider configuration, broken credentials/IAM permissions, the cloud-controller-manager not running/healthy, or a misconfigured cloud provider implementation.

The other options are not real Kubernetes components. Kubernetes does not include a “Load Balancer Manager” or “Cloud Architecture Manager” component name in its standard architecture. In many managed Kubernetes offerings, the cloud-controller-manager (or its equivalent) is provided/managed by the provider, but the responsibility remains the same: reconcile Kubernetes Service resources into cloud load balancer resources.

Therefore, in this scenario, the failing component is theCloud Controller Manager, which is the Kubernetes control plane component that interfaces with the cloud provider to provision external load balancers and update the Service status.

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The verified answer in the PDF isA (HPA), and that aligns with the common Kubernetes meaning of “adjust resources for an application” by scalingreplicas. TheHorizontal Pod Autoscalerautomatically changes the number of Pod replicas for a workload (typically a Deployment) based on observed metrics such as CPU utilization, memory (in some configurations), or custom/external metrics. By increasing replicas under load, the application gains more total CPU/memory capacity available across Pods; by decreasing replicas when load drops, it reduces resource consumption and cost.

It’s important to distinguish what each mechanism adjusts:

HPAadjustsreplica count(horizontal scaling).

VPAadjustsPod resource requests/limits(vertical scaling), which is literally “amount of CPU/memory per pod,” but it often requires restarts to apply changes depending on mode.

Cluster Autoscaleradjusts the number ofnodesin the cluster, not application replicas.

KEDAis event-driven autoscaling that often drives HPA behavior using external event sources (queues, streams), but it’s not the primary built-in mechanism referenced in many foundational Kubernetes questions.

Given the wording and the provided answer key, the intended interpretation is: “automatically adjust the resources available to the application” by scaling out/in the number of replicas. That’s exactly HPA’s role. For example, if CPU utilization exceeds a target (say 60%), HPA computes a higher desired replica count and updates the workload. The Deployment then creates more Pods, distributing load and increasing available compute.

So, within this question set, the verified correct choice isA (Horizontal Pod Autoscaler).

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The correct answer isC (Platform Engineers). In modern DevOps and platform operating models, platform engineering teams build and maintain theshared delivery capabilitiesthat product/application teams use to ship software safely and quickly. This includes CI/CD pipeline templates, standardized build and test automation, artifact management (registries), deployment tooling (Helm/Kustomize/GitOps), secrets management patterns, policy guardrails, and paved-road workflows that reduce cognitive load for developers.

While application developers (B) write the application code and often contribute pipeline steps for their service, the “build, automate, and offer tooling for developer teams” responsibility maps directly to platform engineering: they provide the internal platform that turns Kubernetes and cloud services into a consumable product. This is especially common in Kubernetes-based organizations where you want consistent deployment standards, repeatable security checks, and uniform observability.

Cluster operators (D) typically focus on the health and lifecycle of the Kubernetes clusters themselves: upgrades, node pools, networking, storage, cluster security posture, and control plane reliability. They may work closely with platform engineers, but “continuous delivery tools for developer teams” is broader than cluster operations. Application users (A) are consumers of the software, not builders of delivery tooling.

In cloud-native application delivery, this division of labor is important: platform engineers enablehigher velocity with safetyby automating the software supply chain—builds, tests, scans, deploys, progressive delivery, and rollback. Kubernetes provides the runtime substrate, but the platform team makes it easy and safe for developers to use it repeatedly and consistently across many services.

Therefore,Platform Engineers (C)is the verified correct choice.

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TheContainer Runtime Interface (CRI)exists so Kubernetes can supportpluggable container runtimesbehind a stable interface, which makesCcorrect. In Kubernetes, thekubeletis responsible for managing Pods on a node, but it does not implement container execution itself. Instead, it delegates container lifecycle operations (pull images, create pod sandbox, start/stop containers, fetch logs, exec/attach streaming) to a container runtime through a well-defined API. CRI is that API contract.

Because of CRI, Kubernetes can run with different container runtimes—commonlycontainerdorCRI-O—without changing kubelet core logic. This improves portability and keeps Kubernetes modular: runtime innovation can happen independently while Kubernetes retains a consistent operational model. CRI is accessed via gRPC and defines the services and message formats kubelet uses to communicate with runtimes.

Option B is incorrect because replication and scaling are handled by controllers (Deployments/ReplicaSets) and schedulers, not by CRI. Option D is incorrect because resource criteria (requests/limits) are expressed in Pod specs and enforced via OS mechanisms (cgroups) and kubelet/runtime behavior, but CRI is not “for defining dynamic resource criteria.” Option A is vague and not the primary statement; while CRI enables runtime integration, its key purpose is explicitly to make runtimespluggableand interoperable.

This design became even more important as Kubernetes moved away from Docker Engine integration (dockershim removal from kubelet). With CRI, Kubernetes focuses on orchestrating Pods, while runtimes focus on executing containers. That separation of responsibilities is a core container orchestration principle and is exactly what the question is testing.

So the verified answer isC.

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The correct answer isB: kubectl create deploy nginx --image=nginx --replicas=2. This uses kubectl create deployment (shorthand create deploy) to generate aDeploymentresource named nginx with the specified container image. The --replicas=2 flag sets the desired replica count, so Kubernetes will create two Pod replicas (via a ReplicaSet) and keep that number stable.

Option A is incorrect because kubectl run is primarily intended to run a Pod (and in older versions could generate other resources, but it’s not the recommended/consistent way to create a Deployment in modern kubectl usage). Option C is invalid syntax: kubectl subcommand order is incorrect; you don’t say kubectl create nginx deployment. Option D uses a non-existent --count flag for Deployment replicas.

From a Kubernetes fundamentals perspective, this question tests two ideas: (1) Deployments are the standard controller for running stateless workloads with a desired number of replicas, and (2) kubectl create deployment is a common imperative shortcut for generating that resource. After running the command, you can confirm with kubectl get deploy nginx, kubectl get rs, and kubectl get pods -l app=nginx (label may vary depending on kubectl version). You’ll see a ReplicaSet created and two Pods brought up.

In production, teams typically use declarative manifests (kubectl apply -f) or GitOps, but knowing the imperative command is useful for quick labs and validation. The key is that replicas are managed by the controller, not by manually starting containers—Kubernetes reconciles the state continuously.

Therefore,Bis the verified correct command.

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In Kubernetes governance and project structure,SIGstands forSpecial Interest Group, soAis correct. Kubernetes is a large open source project under the Cloud Native Computing Foundation (CNCF), and its work is organized into groups that focus on specific domains—such as networking, storage, node, scheduling, security, docs, testing, and many more. SIGs provide a scalable way to coordinate contributors, prioritize work, review design proposals (KEPs), triage issues, and manage releases in their area.

Each SIG typically has regular meetings, mailing lists, chat channels, and maintainers who guide the direction of that part of the project. For example, SIG Network focuses on Kubernetes networking architecture and components, SIG Storage on storage APIs and CSI integration, and SIG Scheduling on scheduler behavior and extensibility. This structure helps Kubernetes evolve while maintaining quality, review rigor, and community-driven decision making.

The other options are not part of Kubernetes project terminology. “Software Installation Guide” and the others might sound plausible, but they are not how Kubernetes defines SIGs.

Understanding SIGs matters operationally because many Kubernetes features and design changes originate from SIGs. When you read Kubernetes enhancement proposals, release notes, or documentation, you’ll often see SIG ownership and references. In short,SIGs are the primary organizational units for Kubernetes engineering and stewardship, and SIG =Special Interest Group.