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[General] KCNA Valid Braindumps Book | KCNA Exam Engine

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【General】 KCNA Valid Braindumps Book | KCNA Exam Engine

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Linux Foundation is a non-profit organization that provides support and resources for open-source software projects. One of its most popular offerings is the Kubernetes and Cloud Native Associate (KCNA) certification exam. Kubernetes and Cloud Native Associate certification is designed for professionals who want to demonstrate their expertise in managing and deploying cloud-native applications using Kubernetes.
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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q106-Q111):NEW QUESTION # 106
Which of the following options is true about considerations for large Kubernetes clusters?
  • A. Kubernetes supports up to 50 nodes and recommends no more than 1000 containers per node.
  • B. Kubernetes supports up to 5000 nodes and recommends no more than 500 Pods per node.
  • C. Kubernetes supports up to 5000 nodes and recommends no more than 110 Pods per node.
  • D. Kubernetes supports up to 1000 nodes and recommends no more than 1000 containers per node.
Answer: C
Explanation:
The correct answer is C: Kubernetes scalability guidance commonly cites support up to 5000 nodes and recommends no more than 110 Pods per node. The "110 Pods per node" recommendation is a practical limit based on kubelet, networking, and IP addressing constraints, as well as performance characteristics for scheduling, service routing, and node-level resource management. It is also historically aligned with common CNI/IPAM defaults where node Pod CIDRs are sized for ~110 usable Pod IPs.
Why the other options are incorrect: A and D reference "containers per node," which is not the standard sizing guidance (Kubernetes typically discusses Pods per node). B's "500 Pods per node" is far above typical recommended limits for many environments and would stress IPAM, kubelet, and node resources significantly.
In large clusters, several considerations matter beyond the headline limits: API server and etcd performance, watch/list traffic, controller reconciliation load, CoreDNS scaling, and metrics/observability overhead. You must also plan for IP addressing (cluster CIDR sizing), node sizes (CPU/memory), and autoscaling behavior. On each node, kubelet and the container runtime must handle churn (starts/stops), logging, and volume operations. Networking implementations (kube-proxy, eBPF dataplanes) also have scaling characteristics.
Kubernetes provides patterns to keep systems stable at scale: request/limit discipline, Pod disruption budgets, topology spread constraints, namespaces and quotas, and careful observability sampling. But the exam-style fact this question targets is the published scalability figure and per-node Pod recommendation.
Therefore, the verified true statement among the options is C.

NEW QUESTION # 107
A ________ is an application running on kubernetes.
  • A. workload
  • B. node
  • C. pod
  • D. container
Answer: A
Explanation:
https://kubernetes.io/docs/concepts/workloads/


NEW QUESTION # 108
Which GitOps engine can be used to orchestrate parallel jobs on Kubernetes?
  • A. Flux
  • B. Jenkins X
  • C. Flagger
  • D. Argo Workflows
Answer: D
Explanation:
Argo Workflows (D) is the correct answer because it is a Kubernetes-native workflow engine designed to define and run multi-step workflows-often with parallelization-directly on Kubernetes. Argo Workflows models workflows as DAGs (directed acyclic graphs) or step-based sequences, where each step is typically a Pod. Because each step is expressed as Kubernetes resources (custom resources), Argo can schedule many tasks concurrently, control fan-out/fan-in patterns, and manage dependencies between steps (e.g., "run these 10 jobs in parallel, then aggregate results").
The question calls it a "GitOps engine," but the capability being tested is "orchestrate parallel jobs." Argo Workflows fits because it is purpose-built for running complex job orchestration, including parallel tasks, retries, timeouts, artifacts passing, and conditional execution. In practice, many teams store workflow manifests in Git and apply GitOps practices around them, but the distinguishing feature here is the workflow orchestration engine itself.
Why the other options are not best:
Flux (C) is a GitOps controller that reconciles cluster state from Git; it doesn't orchestrate parallel job graphs as its core function.
Flagger (B) is a progressive delivery operator (canary/blue-green) often paired with GitOps and service meshes/Ingress; it's not a general workflow orchestrator for parallel batch jobs.
Jenkins X (A) is CI/CD-focused (pipelines), not primarily a Kubernetes-native workflow engine for parallel job DAGs in the way Argo Workflows is.
So, the Kubernetes-native tool specifically used to orchestrate parallel jobs and workflows is Argo Workflows (D).

NEW QUESTION # 109
You are building a web application that needs to serve traffic through a load balancer. Which Kubernetes resource should you use to expose the application service externally?
  • A. Ingress
  • B. ConfigMaps
  • C. Deployments
  • D. StatefulSets
  • E. Services
Answer: A
Explanation:
Ingress is a Kubernetes resource used to define rules for how external traffic is routed to different services within your cluster. It allows you to configure load balancers, SSL termination, and other advanced routing features.

NEW QUESTION # 110
Which Kubernetes component is the smallest deployable unit of computing?
  • A. Container
  • B. Deployment
  • C. StatefulSet
  • D. Pod
Answer: D
Explanation:
In Kubernetes, the Pod is the smallest deployable and schedulable unit, making C correct. Kubernetes does not schedule individual containers directly; instead, it schedules Pods, each of which encapsulates one or more containers that must run together on the same node. This design supports both single-container Pods (the most common) and multi-container Pods (for sidecars, adapters, and co-located helper processes).
Pods provide shared context: containers in a Pod share the same network namespace (one IP address and port space) and can share storage volumes. This enables tight coupling where needed-for example, a service mesh proxy sidecar and the application container communicate via localhost, or a log-forwarding sidecar reads logs from a shared volume. Kubernetes manages lifecycle at the Pod level: kubelet ensures the containers defined in the PodSpec are running and uses probes to determine readiness and liveness.
StatefulSet and Deployment are controllers that manage sets of Pods. A Deployment manages ReplicaSets for stateless workloads and provides rollout/rollback features; a StatefulSet provides stable identities, ordered operations, and stable storage for stateful replicas. These are higher-level constructs, not the smallest units.
Option D ("Container") is smaller in an abstract sense, but it is not the smallest Kubernetes deployable unit because Kubernetes APIs and scheduling work at the Pod boundary. You don't "kubectl apply" a container; you apply a Pod template within a Pod object (often via controllers).
Understanding Pods as the atomic unit is crucial: Services select Pods, autoscalers scale Pods (replica counts), and scheduling decisions are made per Pod. That's why Kubernetes documentation consistently refers to Pods as the fundamental building block for running workloads.

NEW QUESTION # 111
......
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