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

The correct answer isB: GitOps Toolkit. Flux is a GitOps solution for Kubernetes, and in Flux v2 the project is built as a set of Kubernetes controllers and supporting components collectively referred to as theGitOps Toolkit. This toolkit provides the building blocks for implementing GitOps reconciliation: sourcing artifacts (Git repositories, Helm repositories, OCI artifacts), applying manifests (Kustomize/Helm), and continuously reconciling cluster state to match the desired state declared in Git.

This construction matters because it reflects Flux’s modular architecture. Instead of being a single monolithic daemon, Flux is composed of controllers that each handle a part of the GitOps workflow: fetching sources, rendering configuration, and applying changes. This makes it more Kubernetes-native: everything is declarative, runs in the cluster, and can be managed like other workloads (RBAC, namespaces, upgrades, observability).

Why the other options are wrong:

“GitLab Environment Toolkit” and “GitHub Actions Toolkit” are not what Flux is built from. Flux can integrate with many SCM providers and CI systems, but it is not “constructed with” those.

“Helm Toolkit” is not the named foundational set Flux is built upon. Flux can deploy Helm charts, but that’s a capability, not its underlying construction.

In cloud-native delivery, Flux implements the key GitOps control loop: detect changes in Git (or other declared sources), compute desired Kubernetes state, and apply it while continuously checking for drift. The GitOps Toolkit is the set of controllers enabling that loop.

Therefore, the verified correct answer isB.

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The correct answer isB. In Kubernetes API versioning, the stability level is encoded in the version string:alpha,beta, andstable (v1). A version like v2beta3 indicates the API is in abetastage. Beta APIs are more mature than alpha, but they are not fully guaranteed stable in perpetuity the way v1 stable APIs are intended to be. The key implication is that while beta APIs are generally usable, they can still undergoincompatible changesin future releases as the API design evolves.

Option B captures that meaning: a beta API may change in ways that break compatibility. This is why teams should treat beta APIs with some caution in production: verify upgrade plans, monitor deprecation notices, and be prepared to adjust manifests or client code when moving between Kubernetes versions.

Why the other options are incorrect:

A implies permanence across all future releases in a major version, which is not a beta guarantee. Kubernetes has deprecation and graduation processes, but beta does not equal “forever.”

C overstates safety; beta is typically “tested and enabled by default” for some features, but it’s not the same as stable API guarantees.

D is too vague and misaligned. While any software may contain bugs, the defining point of “beta API” is aboutstability/compatibility guarantees, not merely “bugs.”

In practice, Kubernetes communicates API lifecycle clearly: alpha is experimental and may be disabled by default; beta is feature-complete-ish but may change; stable v1 is strongly compatibility-focused with formal deprecation policies. So, a v2beta3 API signals: usable, but not fully locked—henceB.

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Under the KubernetesPod Security Standards(PSS), theRestrictedprofile is the most locked-down baseline intended to reduce container privilege and host attack surface. In that profile, adding Linux capabilities is generally prohibited except for very limited cases. Among the listed capabilities,NET_BIND_SERVICEis the one commonly permitted in restricted-like policies, soDis correct.

NET_BIND_SERVICEallows a process to bind to “privileged” ports below 1024 (like 80/443) without running as root. This aligns with restricted security guidance: applications should run as non-root, but still sometimes need to listen on standard ports. Allowing NET_BIND_SERVICE enables that pattern without granting broad privileges.

The other capabilities listed are more sensitive and typically not allowed in a restricted profile:CHOWNcan be used to change file ownership,SETUIDrelates to privilege changes and can be abused, andSYS_CHROOTis a broader system-level capability associated with filesystem root changes. In hardened Kubernetes environments, these are normally disallowed because they increase the risk of privilege escalation or container breakout paths, especially if combined with other misconfigurations.

A practical note: exact enforcement depends on the cluster’s admission configuration (e.g., the built-in Pod Security Admission controller) and any additional policy engines (OPA/Gatekeeper). But the security intent of “Restricted” is consistent: run as non-root, disallow privilege escalation, restrict capabilities, and lock down host access. NET_BIND_SERVICE is a well-known exception used to support common application networking needs while staying non-root.

So, the verified correct choice for an allowed capability in Restricted among these options isD: NET_BIND_SERVICE.

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Burst workloads are a classic elasticity problem: you need large capacity for a short window, then very little capacity the rest of the week. The most cost-effective approach in a cloud-based Kubernetes environment is to scale infrastructuredynamically, matching node count to current demand. That’s exactly whatCluster Autoscaleris designed for: it adds nodes when Pods cannot be scheduled due to insufficient resources and removes nodes when they become underutilized and can be drained safely. ThereforeBis correct.

Option A can work operationally, but it commonly results in paying for a reserved “standing army” of nodes that sit idle most of the week—wasteful for bursty patterns unless the nodes are repurposed for other workloads. Taints/tolerations and nodeSelectors are placement tools; they don’t reduce cost by themselves and may increase waste if they isolate nodes. Option D (PriorityClasses) affects which Pods get scheduled firstgiven available capacity, but it does not create capacity. If the cluster doesn’t have enough nodes, high priority Pods will still remain Pending. Option C (reserved instances or committed-use discounts) can reduce unit price, but it assumes relatively predictable baseline usage. For true bursts, you usually want a smaller baseline plus autoscaling, and optionally combine it with discounted capacity types if your cloud supports them.

In Kubernetes terms, the control loop is: batch Jobs create Pods → scheduler tries to place Pods → if many Pods are Pending due to insufficient CPU/memory, Cluster Autoscaler observes this and increases the node group size → new nodes join and kube-scheduler places Pods → after jobs finish and nodes become empty, Cluster Autoscaler drains and removes nodes. This matches cloud-native principles: elasticity, pay-for-what-you-use, and automation. It minimizes idle capacity while still meeting the completion deadline.

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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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In Kubernetes, the actual act ofrunning containerson a node is performed by thecontainer runtime. The kubelet instructs the runtime via CRI, and the runtime pulls images, creates containers, and manages their lifecycle. Among the options provided,CRI-Ois the only container runtime, soBis correct.

It’s important to be precise: the component that “runs containers” is not the control plane and not etcd.etcd(option A) stores cluster state (API objects) as the backing datastore. It never runs containers.cloud-controller-manager(option C) integrates with cloud APIs for infrastructure like load balancers and nodes.kube-controller-manager(option D) runs controllers that reconcile Kubernetes objects (Deployments, Jobs, Nodes, etc.) but does not execute containers on worker nodes.

CRI-O is a CRI implementation that is optimized for Kubernetes and typically uses an OCI runtime (like runc) under the hood to start containers. Another widely used runtime is containerd. The runtime is installed on nodes and is a prerequisite for kubelet to start Pods. When a Pod is scheduled to a node, kubelet reads the PodSpec and asks the runtime to create a “pod sandbox” and then start the container processes. Runtime behavior also includes pulling images, setting up namespaces/cgroups, and exposing logs/stdout streams back to Kubernetes tooling.

So while “the container runtime” is the most general answer, the question’s option list makesCRI-Othe correct selection because it is a container runtime responsible for running containers in Kubernetes.

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The correct answer isB: CustomResourceDefinition (CRD). Kubernetes is designed to be extensible. ACRDlets you define your own resource types (custom API objects) that behave like native Kubernetes resources: they can be created with YAML, stored in etcd, retrieved via the API server, and managed using kubectl. For example, operators commonly define CRDs such as Databases, RedisClusters, or Certificates to model higher-level application concepts.

A CRD extends the API by adding a new kind under a group/version (e.g., example.com/v1). You typically pair CRDs with acontroller(often called an operator) that watches these custom objects and reconciles real-world resources (Deployments, StatefulSets, cloud resources) to match the desired state specified in the CRD instances. This is the same control-loop pattern used for built-in controllers—just applied to your custom domain.

Why the other options aren’t correct: ConfigMaps store configuration data but do not add new API types. A MutatingAdmissionWebhook can modify or validate requests for existing resources, but it doesn’t define new API kinds; it enforces policy or injects defaults. Kustomize is a manifest customization tool (patch/overlay) and doesn’t extend the Kubernetes API surface.

CRDs are foundational to much of the Kubernetes ecosystem: cert-manager, Argo, Istio, and many operators rely heavily on CRDs. They also support schema validation via OpenAPI v3 schemas, which improves safety and tooling (better error messages, IDE hints). Therefore, the mechanism for extending the Kubernetes API isCustomResourceDefinition, optionB.

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A KubernetesServiceis the abstraction that defines a logical set of Pods and the policy for accessing them, soCis correct. Pods are ephemeral: their IPs change as they are recreated, rescheduled, or scaled. A Service solves this by providing a stable endpoint (DNS name and virtual IP) and routing rules that send traffic to the current healthy Pods backing the Service.

A Service typically uses alabel selectorto identify which Pods belong to it. Kubernetes then maintains endpoint data (Endpoints/EndpointSlice) for those Pods and uses the cluster dataplane (kube-proxy or eBPF-based implementations) to forward traffic from the Service IP/port to one of the backend Pod IPs. This is what the question means by “logical set of Pods” and “policy by which to access them” (for example, round-robin-like distribution depending on dataplane, session affinity options, and how ports map via targetPort).

Option A (Selector) is only the query mechanism used by Services and controllers; it is not itself the access abstraction. Option B (Controller) is too generic; controllers reconcile desired state but do not provide stable network access policies. Option D (Job) manages run-to-completion tasks and is unrelated to network access abstraction.

Services can be exposed in different ways: ClusterIP (internal), NodePort, LoadBalancer, and ExternalName. Regardless of type, the core Service concept remains: stable access to a dynamic set of Pods. This is foundational to Kubernetes networking and microservice communication, and it is why Service discovery via DNS works effectively across rolling updates and scaling events.

Thus, the correct answer isService (C).

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Ais the correct answer because a service mesh primarily focuses on securing and managingservice-to-service communication, and a core part of that isauthentication and authorization. In microservices architectures, internal (“east-west”) traffic can become a complex web of calls. A service mesh introduces a dedicated communication layer—commonly implemented with sidecar proxies or node proxies plus a control plane—to apply consistent security and traffic policies across services.

Authentication in a mesh typically meansservice identity: each workload gets an identity (often via certificates), enabling mutual TLS (mTLS) so services can verify each other and encrypt traffic in transit. Authorization then builds on identity to enforce “who can talk to whom” via policies (for example: service A can call service B only on certain paths or methods). These capabilities are central because they reduce the need for every development team to implement and maintain custom security libraries correctly.

Why the other answers are incorrect:

B(data distribution/replication) is a storage/database concern, not a mesh function.

C(vulnerability scanning) is typically part of CI/CD and supply-chain security tooling, not service-to-service runtime traffic management.

D(configuration management) is broader (GitOps, IaC, Helm/Kustomize); a mesh does have configuration, but “configuration management” is not the defining core feature tested here.

Service meshes also commonly provide traffic management (timeouts, retries, circuit breaking, canary routing) and telemetry (metrics/traces), but among the listed options,authentication and authorizationbest matches “core features.” It captures the mesh’s role in standardizing secure communications in a distributed system.

So, the verified correct answer isA.

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The correct answer isC: metrics.k8s.io. Kubernetes’metrics-serveris the standard component that providesresource metrics(primarily CPU and memory) for nodes and pods. It aggregates this information (sourced from kubelet/cAdvisor) and serves it through the Kubernetes aggregated API under the groupmetrics.k8s.io. This is what enables commands like kubectl top nodes and kubectl top pods, and it is also a key data source for autoscaling with the Horizontal Pod Autoscaler (HPA) when scaling on CPU/memory utilization.

Why the other options are wrong:

custom.k8s.io is not the standard API group for metrics-server resource metrics. Custom metrics are typically served through the custom metrics API (commonly custom.metrics.k8s.io) via adapters (e.g., Prometheus Adapter), not metrics-server.

resources.k8s.io is not the metrics-server API group.

cadvisor.k8s.io is not exposed as a Kubernetes aggregated metrics API. cAdvisor is a component integrated into kubelet that provides container stats, but metrics-server is the thing that exposes the aggregated Kubernetes metrics API, and the canonical group is metrics.k8s.io.

Operationally, it’s important to understand the boundary: metrics-server providesbasic resource metricssuitable for core autoscaling and “top” views, but it is not a full observability system (it does not store long-term metrics history like Prometheus). For richer metrics (SLOs, application metrics, long-term trending), teams typically deploy Prometheus or a managed monitoring backend. Still, when the question asks specifically which API exposes metrics-server data, the answer is definitivelymetrics.k8s.io.

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