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The KCNA Exam is designed to test an individual's knowledge and understanding of Kubernetes and cloud-native technologies. Kubernetes is an open-source container orchestration platform that is used to automate the deployment, scaling, and management of containerized applications. Cloud-native technologies, on the other hand, are a set of practices and tools that are used to build and deploy applications in a cloud-native environment.
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Linux Foundation KCNA Certification Exam covers a wide range of topics related to Kubernetes and cloud native technologies, including containerization, Kubernetes architecture, deployment and scaling, troubleshooting, and security. KCNA exam comprises of 50 multiple-choice questions that must be completed within 90 minutes. Candidates who pass the exam will receive a digital badge that can be displayed on their LinkedIn profile or other professional networking sites, demonstrating their expertise in Kubernetes and cloud native technologies. Kubernetes and Cloud Native Associate certification is an excellent way for individuals to enhance their career prospects in the rapidly growing field of cloud-native computing.
NEW QUESTION # 165
What is the main purpose of a DaemonSet?
Answer: C
Explanation:
A DaemonSet ensures that a copy of a specific Pod runs on all or selected nodes in the cluster, typically for background or node-level services.
NEW QUESTION # 166
What Kubernetes control plane component exposes the programmatic interface used to create, manage and interact with the Kubernetes objects?
Answer: C
Explanation:
The kube-apiserver is the front door of the Kubernetes control plane and exposes the programmatic interface used to create, read, update, delete, and watch Kubernetes objects-so C is correct. Every interaction with cluster state ultimately goes through the Kubernetes API. Tools like kubectl, client libraries, GitOps controllers, operators, and core control plane components (scheduler and controllers) all communicate with the API server to submit desired state and to observe current state.
The API server is responsible for handling authentication (who are you?), authorization (what are you allowed to do?), and admission control (should this request be allowed and possibly mutated/validated?). After a request passes these gates, the API server persists the object's desired state to etcd (the backing datastore) and returns a response. The API server also provides a watch mechanism so controllers can react to changes efficiently, enabling Kubernetes' reconciliation model.
It's important to distinguish this from the other options. etcd stores cluster data but does not expose the cluster's primary user-facing API; it's an internal datastore. kube-controller-manager runs control loops (controllers) that continuously reconcile resources (like Deployments, Nodes, Jobs) but it consumes the API rather than exposing it. kube-proxy is a node-level component implementing Service networking rules and is unrelated to the control-plane API endpoint.
Because Kubernetes is "API-driven," the kube-apiserver is central: if it is unavailable, you cannot create workloads, update configurations, or even reliably observe cluster state. This is why high availability architectures prioritize multiple API server instances behind a load balancer, and why securing the API server (RBAC, TLS, audit) is a primary operational concern.
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NEW QUESTION # 167
Which command will list the resource types that exist within a cluster?
Answer: D
NEW QUESTION # 168
Which Kubernetes component enforces resource consumption limits on containers?
Answer: C
Explanation:
The kubelet ensures containers run according to their Pod specifications and works with the container runtime and operating-system mechanisms such as cgroups to enforce configured CPU and memory limits.
NEW QUESTION # 169
What components are common in a service mesh?
Answer: D
Explanation:
A service mesh is an architectural pattern that manages service-to-service communication in a microservices environment by inserting a dedicated networking layer. The two most common building blocks you'll see across service mesh implementations are (1) a data plane of proxies and (2) a control plane that configures and manages those proxies-this aligns best with "service proxy and control plane," option D.
In practice, the data plane is usually implemented via sidecar proxies (or sometimes node/ambient proxies) that sit "next to" workloads and handle traffic functions such as mTLS encryption, retries, timeouts, load balancing policies, traffic splitting, and telemetry generation. These proxies can capture inbound and outbound traffic without requiring changes to application code, which is one of the defining benefits of a mesh.
The control plane provides the management layer: it distributes policy and configuration to the proxies (routing rules, security policies, identities/certificates), discovers services/endpoints, and often coordinates certificate rotation and workload identity. In Kubernetes environments, meshes typically integrate with the Kubernetes API for service discovery and configuration.
Option C is close in spirit but uses non-standard wording ("runtime plane" is not a typical service mesh term;
"control plane" is). Options A and B describe capabilities that may exist in a mesh ecosystem (telemetry, circuit breaking), but they are not the universal "core components" across meshes. Tracing/log storage, for example, is usually handled by external observability backends (e.g., Jaeger, Tempo, Loki) rather than being intrinsic "mesh components." So, the most correct and broadly accepted answer is D: service proxy and control plane.
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NEW QUESTION # 170
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