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[General] New KCNA Exam Topics, KCNA Test Questions Pdf

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【General】 New KCNA Exam Topics, KCNA Test Questions Pdf

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q173-Q178):NEW QUESTION # 173
What is etcd used for in Kubernetes?
  • A. Integration with cloud platforms
  • B. Kubernetes API security
  • C. Backend object storage for the Kubernetes API
  • D. Network routing for the cluster
Answer: C
Explanation:
etcd serves as a distributed object store that backs the Kubernetes API.

NEW QUESTION # 174
What helps an organization to deliver software more securely at a higher velocity?
  • A. CI/CD Pipeline
  • B. Kubernetes
  • C. apt-get
  • D. Docker Images
Answer: A
Explanation:
A CI/CD pipeline is a core practice/tooling approach that enables organizations to deliver software faster and more securely, so D is correct. CI (Continuous Integration) automates building and testing code changes frequently, reducing integration risk and catching defects early. CD (Continuous Delivery/Deployment) automates releasing validated builds into environments using consistent, repeatable steps-reducing manual errors and enabling rapid iteration.
Security improves because automation enables standardized checks on every change: static analysis, dependency scanning, container image scanning, policy validation, and signing/verification steps can be integrated into the pipeline. Instead of relying on ad-hoc human processes, security controls become repeatable gates. In Kubernetes environments, pipelines commonly build container images, run tests, publish artifacts to registries, and then deploy via manifests, Helm, or GitOps controllers-keeping deployments consistent and auditable.
Option A (Kubernetes) is a platform that helps run and manage workloads, but by itself it doesn't guarantee secure high-velocity delivery. It provides primitives (rollouts, declarative config, RBAC), yet the delivery workflow still needs automation. Option B (apt-get) is a package manager for Debian-based systems and is not a delivery pipeline. Option C (Docker Images) are artifacts; they improve portability and repeatability, but they don't provide the end-to-end automation of building, testing, promoting, and deploying across environments.
In cloud-native application delivery, the pipeline is the "engine" that turns code changes into safe production releases. Combined with Kubernetes' declarative deployment model (Deployments, rolling updates, health probes), a CI/CD pipeline supports frequent releases with controlled rollouts, fast rollback, and strong auditability. That is exactly what the question is targeting. Therefore, the verified answer is D.
=========

NEW QUESTION # 175
What happens with a regular Pod running in Kubernetes when a node fails?
  • A. By default, a Pod can only be scheduled to the same node when the node fails.
  • B. A new Pod is scheduled on a different node only if it is configured explicitly.
  • C. A new, near-identical Pod but with different UID is scheduled to another node.
  • D. A new Pod with the same UID is scheduled to another node after a while.
Answer: C
Explanation:
B is correct: when a node fails, Kubernetes does not "move" the same Pod instance; instead, a new Pod object (new UID) is created to replace it-assuming the Pod is managed by a controller (Deployment/ReplicaSet, StatefulSet, etc.). A Pod is an API object with a unique identifier (UID) and is tightly associated with the node it's scheduled to via spec.nodeName. If the node becomes unreachable, that original Pod cannot be restarted elsewhere because it was bound to that node.
Kubernetes' high availability comes from controllers maintaining desired state. For example, a Deployment desires N replicas. If a node fails and the replicas on that node are lost, the controller will create replacement Pods, and the scheduler will place them onto healthy nodes. These replacement Pods will be "near-identical" in spec (same template), but they are still new instances with new UIDs and typically new IPs.
Why the other options are wrong:
A is incorrect because the UID does not remain the same-Kubernetes creates a new Pod object rather than reusing the old identity.
C is incorrect; pods are not restricted to the same node after failure. The whole point of orchestration is to reschedule elsewhere.
D is incorrect; rescheduling does not require special explicit configuration for typical controller-managed workloads. The controller behavior is standard. (If it's a bare Pod without a controller, it will not be recreated automatically.) This also ties to the difference between "regular Pod" vs controller-managed workloads: a standalone Pod is not self-healing by itself, while a Deployment/ReplicaSet provides that resilience. In typical production design, you run workloads under controllers specifically so node failure triggers replacement and restores replica count.
Therefore, the correct outcome is B.

NEW QUESTION # 176
Which of these commands is used to retrieve the documentation and field definitions for a Kubernetes resource?
  • A. kubectl api-resources
  • B. kubectl show
  • C. kubectl get --help
  • D. kubectl explain
Answer: D
Explanation:
kubectl explain is the command that shows documentation and field definitions for Kubernetes resource schemas, so A is correct. Kubernetes resources have a structured schema: top-level fields like apiVersion, kind, and metadata, and resource-specific structures like spec and status. kubectl explain lets you explore these structures directly from your cluster's API discovery information, including field types, descriptions, and nested fields.
For example, kubectl explain deployment describes the Deployment resource, and kubectl explain deployment.
spec dives into the spec structure. You can continue deeper, such as kubectl explain deployment.spec.template.
spec.containers to discover container fields. This is especially useful when writing or troubleshooting manifests, because it reduces guesswork and prevents invalid YAML fields that would be rejected by the API server. It also helps when APIs evolve: you can confirm which fields exist in your cluster's current version and what they mean.
The other commands do different things. kubectl api-resources lists resource types and their shortnames, whether they are namespaced, and supported verbs-useful discovery, but not detailed field definitions.
kubectl get --help shows CLI usage help for kubectl get, not the Kubernetes object schema. kubectl show is not a standard kubectl subcommand.
From a Kubernetes "declarative configuration" perspective, correct manifests are critical: controllers reconcile desired state from spec, and subtle field mistakes can change runtime behavior. kubectl explain is a built-in way to learn the schema and write manifests that align with the Kubernetes API's expectations. That's why it' s commonly recommended in Kubernetes documentation and troubleshooting workflows.
=========

NEW QUESTION # 177
Which GitOps engine can be used to orchestrate parallel jobs on Kubernetes?
  • A. Flagger
  • B. Jenkins X
  • C. Argo Workflows
  • D. Flux
Answer: C
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 # 178
......
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