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KCNA Valid Exam Simulator - Valid KCNA Vce

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KCNA Valid Exam Simulator - Valid KCNA Vce

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BTW, DOWNLOAD part of 2Pass4sure KCNA dumps from Cloud Storage: https://drive.google.com/open?id=1gGtfI5ThxsPOj9VNRmyl91GHUouWMIsG
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Linux Foundation KCNA certification is a valuable credential for individuals who are interested in pursuing a career in cloud-native technologies. Kubernetes and Cloud Native Associate certification is recognized by major organizations and is a testament to the candidate's knowledge and skills in Kubernetes and cloud-native technologies. Kubernetes and Cloud Native Associate certification can help candidates stand out in the job market and open up new career opportunities in the field of cloud-native computing. Furthermore, the certification also provides access to a community of Kubernetes and cloud-native experts, which can be a valuable resource for networking and professional development.
Linux Foundation Kubernetes and Cloud Native Associate (KCNA) Certification Exam is a performance-based exam that assesses an individual's knowledge and skills in the field of Kubernetes and cloud native technologies. Kubernetes and Cloud Native Associate certification is designed for those who are new to these technologies or those who have some experience but want to validate their expertise. KCNA Exam covers a wide range of topics, including Kubernetes architecture, deployment, and management, as well as cloud native technologies such as containerization, microservices, and serverless computing.
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The Kubernetes and Cloud Native Associate certification exam is designed for individuals who have basic knowledge of Linux administration and the ability to work with command-line interfaces. KCNA exam covers a broad range of topics, including containerization, container orchestration, Kubernetes networking, storage, security, and troubleshooting. Successful completion of KCNA Exam demonstrates that the candidate has the skills to run and manage Kubernetes clusters and deploy cloud-native applications.
Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q13-Q18):NEW QUESTION # 13
A Pod named my-app must be created to run a simple nginx container. Which kubectl command should be used?
  • A. kubectl create my-app --image=nginx
  • B. kubectl create nginx --name=my-app
  • C. kubectl run nginx --name=my-app
  • D. kubectl run my-app --image=nginx
Answer: D
Explanation:
In Kubernetes, the simplest and most direct way to create a Pod that runs a single container is to use the kubectl run command with the appropriate image specification. The command kubectl run my-app -- image=nginx explicitly instructs Kubernetes to create a Pod named my-app using the nginx container image, which makes option B the correct answer.
The kubectl run command is designed to quickly create and run a Pod (or, in some contexts, a higher-level workload resource) from the command line. When no additional flags such as --restart=Always are specified, Kubernetes creates a standalone Pod by default. This is ideal for simple use cases like testing, demonstrations, or learning scenarios where only a single container is required.
Option A is incorrect because kubectl create nginx --name=my-app is not valid syntax; the create subcommand requires a resource type (such as pod, deployment, or service) or a manifest file. Option C is also incorrect because kubectl create my-app --image=nginx omits the resource type and therefore is not a valid kubectl create command. Option D is incorrect because kubectl run nginx --name=my-app attempts to use the deprecated --name flag, which is no longer supported in modern versions of kubectl.
Using kubectl run with explicit naming and image flags is consistent with Kubernetes command-line conventions and is widely documented as the correct approach for creating simple Pods. The resulting Pod can be verified using commands such as kubectl get pods and kubectl describe pod my-app.
In summary, Option B is the correct and verified answer because it uses valid kubectl syntax to create a Pod named my-app running the nginx container image in a straightforward and predictable way.

NEW QUESTION # 14
Let's assume that an organization needs to process large amounts of data in bursts, on a cloud-based Kubernetes cluster. For instance: each Monday morning, they need to run a batch of 1000 compute jobs of 1 hour each, and these jobs must be completed by Monday night. What's going to be the most cost-effective method?
  • A. Commit to a specific level of spending to get discounted prices (with e.g. "reserved instances" or similar mechanisms).
  • B. Use PriorityClasses so that the weekly batch job gets priority over other workloads running on the cluster, and can be completed on time.
  • C. Run a group of nodes with the exact required size to complete the batch on time, and use a combination of taints, tolerations, and nodeSelectors to reserve these nodes to the batch jobs.
  • D. Leverage the Kubernetes Cluster Autoscaler to automatically start and stop nodes as they're needed.
Answer: D
Explanation:
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 infrastructure dynamically, matching node count to current demand. That's exactly what Cluster Autoscaler is 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. Therefore B is 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 first given 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.
=========

NEW QUESTION # 15
What default level of protection is applied to the data in Secrets in the Kubernetes API?
  • A. The values are base64 encoded
  • B. The values are encoded with SHA256 hashes
  • C. The values are stored in plain text
  • D. The values use AES symmetric encryption
Answer: A
Explanation:
Kubernetes Secrets are designed to store sensitive data such as tokens, passwords, or certificates and make them available to Pods in controlled ways (as environment variables or mounted files). However, the default protection applied to Secret values in the Kubernetes API is base64 encoding, not encryption. That is why D is correct. Base64 is an encoding scheme that converts binary data into ASCII text; it is reversible and does not provide confidentiality.
By default, Secret objects are stored in the cluster's backing datastore (commonly etcd) as base64-encoded strings inside the Secret manifest. Unless the cluster is configured for encryption at rest, those values are effectively stored unencrypted in etcd and may be visible to anyone who can read etcd directly or who has API permissions to read Secrets. This distinction is critical for security: base64 can prevent accidental issues with special characters in YAML/JSON, but it does not protect against attackers.
Option A is only correct if encryption at rest is explicitly configured on the API server using an EncryptionConfiguration (for example, AES-CBC or AES-GCM providers). Many managed Kubernetes offerings enable encryption at rest for etcd as an option or by default, but that is a deployment choice, not the universal Kubernetes default. Option C is incorrect because hashing is used for verification, not for secret retrieval; you typically need to recover the original value, so hashing isn't suitable for Secrets. Option B ("plain text") is misleading: the stored representation is base64-encoded, but because base64 is reversible, the security outcome is close to plain text unless encryption at rest and strict RBAC are in place.
The correct operational stance is: treat Kubernetes Secrets as sensitive; lock down access with RBAC, enable encryption at rest, avoid broad Secret read permissions, and consider external secret managers when appropriate. But strictly for the question's wording-default level of protection-base64 encoding is the right answer.
=========

NEW QUESTION # 16
You have a containerized application that needs to access a specific environment variable. Which of the following methods would you typically use to provide this environment variable within a Kubernetes Pod definition?
  • A. Environment variable within the container image
  • B. ConfigMap
  • C. Secret
  • D. Pod Security Policy
  • E. VolumeMounts
Answer: B,C
Explanation:
Both ConfigMaps and Secrets are Kubernetes resources that allow you to pass configuration data to containers. ConfigMaps store simple key-value pairs, suitable for environment variables, while Secrets are used to store sensitive information like passwords or API keys. In this case, both are valid options to provide environment variables within a Pod.

NEW QUESTION # 17
How do you deploy a workload to Kubernetes without additional tools?
  • A. Create a Bash script and run it on a worker node.
  • B. Create a Helm Chart and install it with helm.
  • C. Create a Python script and run it with kubectl.
  • D. Create a manifest and apply it with kubectl.
Answer: D
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 # 18
......
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