KCNA Exam Dumps - Kubernetes and Cloud Native Associate

The correct answer isA (describe), used as kubectl describe . kubectl describe is a troubleshooting-focused command that provides a rich, human-readable view of a specific live object in the cluster, including key fields, status, and—crucially—Eventsrelated to that object. This makes it extremely useful for “collecting information” about almost any active resource: Pods, Deployments, Nodes, Services, PersistentVolumeClaims, and more.

kubectl get (not listed) is typically used for listing objects and their summary fields, but kubectl describe goes deeper: for a Pod it will show container images, resource requests/limits, probes, mounted volumes, node assignment, IPs, conditions, and recent scheduling/pulling/starting events. For a Node it shows capacity/allocatable resources, labels/taints, conditions, and node events. Those event details often explain why something is Pending, failing to pull images, failing readiness checks, or being evicted.

Option B (“list”) is not a standard kubectl subcommand for retrieving resource information (you would use get for listing). Option C (expose) is for creating a Service to expose a resource (like a Deployment). Option D (explain) is for viewing API schema/field documentation (e.g., kubectl explain deployment.spec.replicas) and does not report what is currently happening in the cluster.

So, for gathering detailed live diagnostics about a resource in the cluster, the best kubectl command iskubectl describe, which corresponds to optionA.

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Dis correct: the mandatory top-level fields for creating a Kubernetes object manifest areapiVersion,kind,metadata, and (for most objects you create)spec. These fields establish what the object is and what you want Kubernetes to do with it.

apiVersiontells Kubernetes which API group/version schema to use (e.g., apps/v1, v1). This determines valid fields and behavior.

kindidentifies the resource type (e.g., Pod, Deployment, Service).

metadatacontains identifying information like name, namespace, and labels/annotations used for organization, selection, and automation.

specdescribes the desired state. Controllers and the kubelet reconcile actual state to match spec.

Why other choices are wrong:

statusis not a mandatory input field. It’s generally written by Kubernetes controllers and reflects observed state (conditions, readiness, assigned node, etc.). Users typically do not set status when creating objects.

templateis not a universal top-level field. It exists inside some resources (notably Deployment.spec.template), but it’s not a required top-level field across Kubernetes objects.

It’s true that some resources can be created without a spec (or with minimal fields), but in the exam-style framing—“mandatory fields… using a YAML file”—the canonical expected set is exactly the four inD. This aligns with how Kubernetes documentation and examples present manifests: identify the API schema and kind, give object metadata, and declare desired state.

Therefore,apiVersion + metadata + kind + specis the only option that includes only the mandatory fields, makingDthe verified correct answer.

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ADeploymentis a higher-level controller that managesReplicaSetsand provides rollout/rollback behavior, soDis correct. A ReplicaSet’s primary job is to ensure that a specified number of Pod replicas are running at any time, based on a label selector and Pod template. It’s a fundamental “keep N Pods alive” controller.

Deployments build on that by managing the lifecycle of ReplicaSets over time. When you update a Deployment (for example, changing the container image tag or environment variables), Kubernetes creates anew ReplicaSetfor the new Pod template and gradually shifts replicas from the old ReplicaSet to the new one according to the rollout strategy (RollingUpdate by default). Deployments also retain revision history, making it possible to roll back to a previous ReplicaSet if a rollout fails.

Why the other options are incorrect:

A is false: Deployments absolutely control the number of replicas via spec.replicas and can also be controlled by HPA.

B is false: ReplicaSetsdoguarantee that a stable number of replicas is running (that is their core purpose).

C is false: a Deployment is not “a ReplicaSet with annotations.” It is a distinct API resource with additional controller logic for declarative updates, rollouts, and revision tracking.

Operationally, most teams create Deployments rather than ReplicaSets directly because Deployments are safer and more feature-complete for application delivery. ReplicaSets still appear in real clusters because Deployments create them automatically; you’ll commonly see multiple ReplicaSets during rollout transitions. Understanding the hierarchy is crucial for troubleshooting: if Pods aren’t behaving as expected, you often trace from Deployment → ReplicaSet → Pod, checking selectors, events, and rollout status.

So the key difference is:ReplicaSet maintains replica count; Deployment manages ReplicaSets and orchestrates updates.Therefore,Dis the verified answer.

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A service mesh is most valuable when service-to-service communication becomes complex at large scale—many services, many teams, and often multiple clusters. That’s whyDis the best fit:thousands of distributed applications across multiple clusters. In that scenario, the operational burden of securing, observing, and controlling east-west traffic grows dramatically. A service mesh (e.g., Istio, Linkerd) addresses this by introducing a dedicated networking layer (usually sidecar proxies such as Envoy) that standardizes capabilities across services without requiring each application to implement them consistently.

The common “mesh” value-adds are:mTLSfor service identity and encryption, fine-grainedtraffic policy(retries, timeouts, circuit breaking),traffic shifting(canary, mirroring), and consistenttelemetry(metrics, traces, access logs). Those features become increasingly beneficial as the number of services and cross-service calls rises, and as you add multi-cluster routing, failover, and policy management across environments. With thousands of applications, inconsistent libraries and configurations become a reliability and security risk; the mesh centralizes and standardizes these behaviors.

In smaller environments (A or C), you can often meet requirements with simpler approaches: Kubernetes Services, Ingress/Gateway, basic mTLS at the edge, and application-level libraries. A single large cluster (B) can still benefit from a mesh, but addingmultiple clustersincreases complexity: traffic management across clusters, identity trust domains, global observability correlation, and consistent policy enforcement. That’s where mesh architectures typically justify their additional overhead (extra proxies, control plane components, operational complexity).

So, the “most benefit” scenario is the largest, most distributed footprint—D.

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Serverless computingis a cloud execution model where the provider manages infrastructure concerns and you consume compute as a service, typically billed based on actual usage (requests, execution time, memory), which matchesA. In other words, you deploy code (functions) or sometimes containers, configure triggers (HTTP events, queues, schedules), and the platform automatically provisions capacity, scales it up/down, and handles much of availability and fault tolerance behind the scenes.

From a cloud-native architecture standpoint, “serverless” doesn’t mean there are no servers; it meansdevelopers don’t manage servers. The platform abstracts away node provisioning, OS patching, and much of runtime scaling logic. This aligns with the “as-used basis” phrasing: you pay for what you run rather than maintaining always-on capacity.

It’s also useful to distinguish serverless from Kubernetes. Kubernetes automates orchestration (scheduling, self-healing, scaling), but operating Kubernetes still involves cluster-level capacity decisions, node pools, upgrades, networking baseline, and policy. With serverless, those responsibilities are pushed further toward the provider/platform. Kubernetes canenableserverless experiences (for example, event-driven autoscaling frameworks), but serverless as a model is about a higher level of abstraction than “orchestrate containers yourself.”

Options B, C, and D are incorrect because they describe specialized or vague “operating system” services rather than the commonly accepted definition. Serverless is not specifically about AI/ML OSs or quantum OSs; it’s a general compute delivery model that can host many kinds of workloads.

Therefore, the correct definition in this question isA: providing backend services on an as-used basis.

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Dynamic provisioning is the Kubernetes mechanism where storage iscreated on-demandwhen a user creates aPersistentVolumeClaim (PVC)that references aStorageClass, soAis correct. In this model, the user does not need to pre-create a PersistentVolume (PV). Instead, the StorageClass points to a provisioner (typically a CSI driver) that knows how to create a volume in the underlying storage system (cloud disk, SAN, NAS, etc.). When the PVC is created with storageClassName: , Kubernetes triggers the provisioner to create a new volume and then binds the resulting PV to that PVC.

This is why option B is incorrect: you do not put a StorageClass “in the Pod YAML” to request provisioning. Pods reference PVCs, not StorageClasses directly. Option C is incorrect because the PVC does not need the Pod name; binding is done via the PVC itself. Option D describesstatic provisioning: an admin pre-creates PVs and users claim them by creating PVCs that match the PV (capacity, access modes, selectors). Static provisioning can work, but it is not dynamic provisioning.

Under the hood, the StorageClass can define parameters like volume type, replication, encryption, and binding behavior (e.g., volumeBindingMode: WaitForFirstConsumer to delay provisioning until the Pod is scheduled, ensuring the volume is created in the correct zone). Reclaim policies (Delete/Retain) define what happens to the underlying volume after the PVC is deleted.

In cloud-native operations, dynamic provisioning is preferred because it improves developer self-service, reduces manual admin work, and makes scaling stateful workloads easier and faster. The essence is:PVC + StorageClass → automatic PV creation and binding.

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Kubernetes API versions follow a consistent naming pattern that indicates stability level and versioning. The valid forms include stable versions likev1, and pre-release versions such asv1alpha1,v1beta1, etc. OptionCcontains valid-looking Kubernetes version strings—v1alpha1, v2beta3, v2—soCis correct.

In Kubernetes, the “v” prefix is part of the standard for API versions. A stable API uses v1, v2, etc. Pre-release APIs include a stability marker: alpha (earliest, most changeable) and beta (more stable but still may change). The numeric suffix (e.g., alpha1, beta3) indicates iteration within that stability stage.

Option A is invalid because strings like alpha1v1 and beta3v3 do not match Kubernetes conventions (the v comes first, and alpha/beta are qualifiers after the version: v1alpha1). Option B is invalid because alpha1 and beta3 are missing the leading version prefix; Kubernetes API versions are not just “alpha1.” Option D includes 2.0, which looks like semantic versioning but is not the Kubernetes API version format. Kubernetes uses v2, not 2.0, for API versions.

Understanding this matters because API versions signal compatibility guarantees. Stable APIs are supported for a defined deprecation window, while alpha/beta APIs may change in incompatible ways and can be removed more easily. When authoring manifests, selecting the correct apiVersion ensures the API server accepts your resource and that controllers interpret fields correctly.

Therefore, among the choices,Cis the only option comprised of valid Kubernetes-style API version strings.

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Ais correct: Kubernetes’ API can be extended by addingCustomResourceDefinitions (CRDs)and/or by implementing theAPI Aggregation Layer. These are the two canonical extension mechanisms.

CRDslet you define new resource types (new kinds) that the Kubernetes API server stores in etcd and serves like native objects. You typically pair a CRD with a controller/operator that watches those custom objects and reconciles real resources accordingly. This pattern is foundational to the Kubernetes ecosystem (many popular add-ons install CRDs).

Theaggregation layerallows you to add entire API services (aggregated API servers) that serve additional endpoints under the Kubernetes API. This is used when you want custom API behavior, custom storage, or specialized semantics beyond what CRDs provide (or when implementing APIs like metrics APIs historically).

Why the other answers are wrong:

Bis not how API extension works. You don’t “extend the API” by inventing new versions like v4beta3; versions are defined and implemented by API servers/controllers, not by users arbitrarily.

Cis fictional; there is no standard kubectl extend api command.

Dis also incorrect; kubelet parameters configure node agent behavior, not API server types and discovery.

So, the verified ways to extend Kubernetes’ API surface areCRDs and API aggregation, which is optionA.

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