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| Certification Vendor: | Linux Foundation |
|---|---|
| Exam Name: | Kubernetes and Cloud Native Associate |
| Exam Number: | KCNA |
| Exam Duration: | 90 minutes |
| Exam Price: | $250 USD |
| Certificate Validity Period: | 3 years |
| Related Certifications: | CKAD (Certified Kubernetes Application Developer) CKA (Certified Kubernetes Administrator) CKS (Certified Kubernetes Security Specialist) |
| Real Exam Qty: | 60 |
| Passing Score: | 75% (approximately 45 out of 60) |
| Exam Format: | Multiple Choice |
| Available Languages: | Japanese, English |
| Sample Questions: | Linux Foundation KCNA Sample Questions |
| Exam Way: | Online, proctored exam |
| Pre Condition: | None. No prerequisites required. |
| Official Syllabus URL: | https://training.linuxfoundation.org/certification/kubernetes-cloud-native-associate |
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Linux Foundation KCNA Certification Exam is ideal for individuals who are interested in pursuing a career in cloud computing or DevOps. It is also suitable for IT professionals who want to enhance their skills in Kubernetes and cloud native technologies to support their organization's digital transformation initiatives. KCNA Exam covers a wide range of topics, including Kubernetes architecture, deployment, networking, storage, security, and troubleshooting.
NEW QUESTION # 128
What methods can you use to scale a Deployment?
Answer: D
Explanation:
A Deployment's replica count is controlled by spec.replicas. You can scale a Deployment by changing that field-either directly editing the object or using kubectl's scaling helper. Therefore C is correct: you can scale using kubectl scale and also via kubectl edit.
kubectl scale deployment <name> --replicas=<n> is the purpose-built command for scaling. It updates the Deployment's desired replica count and lets the Deployment controller and ReplicaSet reconcile the change by creating or deleting Pods. This is the cleanest and most explicit operational approach, and it's easy to automate in scripts and pipelines.
kubectl edit deployment <name> opens the live object in an editor, allowing you to modify fields such as spec.replicas manually. When you save and exit, kubectl submits the updated object back to the API server. This method is useful for quick interactive adjustments or when you're already making other spec edits, but it's less structured and more error-prone than kubectl scale for simple replica changes.
Option B is invalid because kubectl scale-up deployment is not a standard kubectl command. Option A is incorrect because kubectl edit is not the only method; scaling is commonly done with kubectl scale. Option D is also incorrect because while kubectl scale is a primary method, kubectl edit is also a valid method to change replicas.
In production, you often scale with autoscalers (HPA/VPA), but the question is asking about kubectl methods. The key Kubernetes concept is that scaling is achieved by updating desired state (spec.replicas), and controllers reconcile Pods to match.
NEW QUESTION # 129
Which of the following is used to request storage in Kubernetes?
Answer: D
Explanation:
https://kubernetes.io/docs/concepts/storage/persistent-volumes/
NEW QUESTION # 130
Which of the following is a feature Kubernetes provides by default as a container orchestration tool?
Answer: A
Explanation:
Kubernetes provides automated rollouts and rollbacks for workloads by default (via controllers like Deployments), so D is correct. In Kubernetes, application delivery is controller-driven: you declare the desired state (new image, new config), and controllers reconcile the cluster toward that state. Deployments implement rolling updates, gradually replacing old Pods with new ones while respecting availability constraints. Kubernetes tracks rollout history and supports rollback to previous ReplicaSets when an update fails or is deemed unhealthy.
This is a core orchestration capability: it reduces manual intervention and makes change safer. Rollouts use readiness checks and update strategies to avoid taking the service down, and kubectl rollout status/history
/undo supports day-to-day release operations.
The other options are not "default Kubernetes orchestration features":
* Kubernetes is not a portable operating system (A). It's a platform for orchestrating containers on top of an OS.
* Kubernetes does not provide filesystem redundancy by itself (B). Storage redundancy is handled by underlying storage systems and CSI drivers (e.g., replicated block storage, distributed filesystems).
* Kubernetes does not include a built-in container image registry (C). You use external registries (Docker Hub, ECR, GCR, Harbor, etc.). Kubernetes pulls images but does not host them as a core feature.
So the correct "provided by default" orchestration feature in this list is the ability to safely manage application updates via automated rollouts and rollbacks.
=========
NEW QUESTION # 131
Which cloud native tool(s) keeps Kubernetes clusters in sync with sources of configuration (like Git repositories), and automates updates to configuration when there is new code to deploy?
Answer: B
Explanation:
Flux and Argo CD are GitOps tools that continuously monitor configuration sources such as Git repositories and automatically reconcile the Kubernetes cluster state to match them, ensuring deployments stay synchronized and updated.
NEW QUESTION # 132
How do you deploy a workload to Kubernetes without additional tools?
Answer: B
Explanation:
The standard way to deploy workloads to Kubernetes using only built-in tooling is to create Kubernetes manifests (YAML/JSON definitions of API objects) and apply them with kubectl, so C is correct. Kubernetes is a declarative system: you describe the desired state of resources (e.g., a Deployment, Service, ConfigMap, Ingress) in a manifest file, then submit that desired state to the API server. Controllers reconcile the actual cluster state to match what you declared.
A manifest typically includes mandatory fields like apiVersion, kind, and metadata, and then a spec describing desired behavior. For example, a Deployment manifest declares replicas and the Pod template (containers, images, ports, probes, resources). Applying the manifest with kubectl apply -f <file> creates or updates the resources. kubectl apply is also designed to work well with iterative changes: you update the file, re-apply, and Kubernetes performs a controlled rollout based on controller logic.
Option B (Helm) is indeed a popular deployment tool, but Helm is explicitly an "additional tool" beyond kubectl and the Kubernetes API. The question asks "without additional tools," so Helm is excluded by definition. Option A (running Bash scripts on worker nodes) bypasses Kubernetes' desired-state control and is not how Kubernetes workload deployment is intended; it also breaks portability and operational safety. Option D is not a standard Kubernetes deployment mechanism; kubectl does not "run Python scripts" to deploy workloads (though scripts can automate kubectl, that's still not the primary mechanism).
From a cloud native delivery standpoint, manifests support GitOps, reviewable changes, and repeatable deployments across environments. The Kubernetes-native approach is: declare resources in manifests and apply them to the cluster. Therefore, C is the verified correct answer.
NEW QUESTION # 133
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