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[General] New Valid Exam KCNA Preparation | High Pass-Rate New KCNA Exam Papers: Kubernete

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【General】 New Valid Exam KCNA Preparation | High Pass-Rate New KCNA Exam Papers: Kubernete

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Linux Foundation KCNA Certification Exam is a rigorous, performance-based exam that tests the candidate's ability to perform real-world tasks in Kubernetes and cloud native environments. KCNA exam is conducted online, and candidates are required to complete a set of hands-on tasks within a specified time frame. KCNA Exam is designed to assess the candidate's ability to deploy, manage, and troubleshoot Kubernetes clusters and cloud native applications.
Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q117-Q122):NEW QUESTION # 117
You're building a serverless application on AWS Lambd
a. How can you manage dependencies for your Lambda functions?
  • A. Include dependencies in the Lambda function's deployment package.
  • B. Utilize AWS CodeDeploy to manage dependency updates for Lambda functions-
  • C. Use AWS Lambda Layers to organize and manage dependencies for Lambda functions.
  • D. Use Docker containers to package and deploy Lambda functions with their dependencies.
  • E. Install dependencies directly on the Lambda execution environment
Answer: A,C
Explanation:
The most common and recommended approaches for managing dependencies in AWS Lambda are to include them in the deployment package (C) and to use AWS Lambda Layers (E). Layers allow you to organize and share dependencies across multiple Lambda functions, promoting code reusability and reducing deployment size. Docker containers (A) are not directly supported for Lambda function deployments- CodeDeploy (B) is primarily for managing deployments, not dependencies themselves. Installing dependencies directly on the Lambda execution environment (D) is not possible and can lead to conflicts.

NEW QUESTION # 118
What is Helm?
  • A. A package manager for Kubernetes applications.
  • B. An open source dashboard for Kubernetes.
  • C. A custom scheduler for Kubernetes.
  • D. An end-to-end testing project for Kubernetes applications.
Answer: A
Explanation:
Helm is best described as a package manager for Kubernetes applications, making B correct. Helm packages Kubernetes resource manifests (Deployments, Services, ConfigMaps, Ingress, RBAC, etc.) into a unit called a chart. A chart includes templates and default values, allowing teams to parameterize deployments for different environments (dev/stage/prod) without rewriting YAML.
From an application delivery perspective, Helm solves common problems: repeatable installation, upgrade management, versioning, and sharing of standardized application definitions. Instead of copying and editing raw YAML, users install a chart and supply a values.yaml file (or CLI overrides) to configure image tags, replica counts, ingress hosts, resource requests, and other settings. Helm then renders templates into concrete Kubernetes manifests and applies them to the cluster.
Helm also manages releases: it tracks what has been installed and supports upgrades and rollbacks. This aligns with cloud native delivery practices where deployments are automated, reproducible, and auditable. Helm is commonly integrated into CI/CD pipelines and GitOps workflows (sometimes with charts stored in Git or Helm repositories).
The other options are incorrect: a dashboard is a UI like Kubernetes Dashboard; a scheduler is kube-scheduler (or custom scheduler implementations, but Helm is not that); end-to-end testing projects exist in the ecosystem, but Helm's role is packaging and lifecycle management of Kubernetes app definitions.
So the verified, standard definition is: Helm = Kubernetes package manager.
________________________________________

NEW QUESTION # 119
You have a Kubernetes cluster with a custom network plugin that provides advanced networking capabilities. What needs to be considered when configuring this custom network plugin?
  • A. Test the plugin thoroughly with your applications to ensure compatibility and performance.
  • B. Integrate the plugin with the Kubernetes API server to manage network resources.
  • C. Configure the plugin to support Kubernetes service discovery and load balancing.
  • D. Ensure that the custom plugin adheres to the Kubernetes CNI (Container Network Interface) specification.
  • E. Deploy the plugin as a DaemonSet on each Kubernetes node.
Answer: A,B,C,D,E
Explanation:
All of the options are important considerations when configuring a custom network plugin in Kubernetes- - A Adherence to the CNI specification ensures interoperability with Kubernetes and other CNI plugins- - B: Support for service discovery and load balancing is crucial for seamless integration with Kubernetes services. - C: Integration with the Kubernetes API server allows the plugin to be managed and controlled by Kubernetes- - D Deploying the plugin as a DaemonSet ensures its availability and consistent behavior across all nodes. - E Thorough testing is essential to guarantee compatibility with your applications and the desired performance characteristics.

NEW QUESTION # 120
You have a Kubernetes cluster running on AWS. You need to ensure that only approved container images are used in your cluster. Which Kubernetes feature can you use to enforce this policy?
  • A. Admission Controllers
  • B. Resource Quotas
  • C. Network Policies
  • D. Service Accounts
  • E. Pod Security Policies
Answer: A
Explanation:
Admission Controllers in Kubernetes can be used to enforce policies for container images. You can configure an Admission Controller to check if the image is present in an approved image registry or if it meets certain security criteria. This helps prevent unauthorized or insecure images from being deployed to your cluster.

NEW QUESTION # 121
What is a Kubernetes Service Endpoint?
  • A. It is an IP address that we can access from the Internet.
  • B. It is the API endpoint of our Kubernetes cluster.
  • C. It is an object that gets IP addresses of individual Pods assigned to it.
  • D. It is a name of special Pod in kube-system namespace.
Answer: C
Explanation:
A Kubernetes Service routes traffic to a dynamic set of backends (usually Pods). The set of backend IPs and ports is represented by endpoint-tracking resources. Historically this was the Endpoints object; today Kubernetes commonly uses EndpointSlice for scalability, but the concept remains the same: endpoints represent the concrete network destinations behind a Service. That's why D is correct: a Service endpoint is an object that contains the IP addresses (and ports) of the individual Pods (or other backends) associated with that Service.
When a Service has a selector, Kubernetes automatically maintains endpoints by watching which Pods match the selector and are Ready, then publishing those Pod IPs into Endpoints/EndpointSlices. Consumers don't usually use endpoints directly; instead they call the Service DNS name, and kube-proxy (or an alternate dataplane) forwards traffic to one of the endpoints. Still, endpoints are critical because they are what make Service routing accurate and up to date during scaling events, rolling updates, and failures.
Option A confuses this with the Kubernetes API server endpoint (the cluster API URL). Option B is incorrect; there's no special "Service Endpoint Pod." Option C describes an external/public IP concept, which may exist for LoadBalancer Services, but "Service endpoint" in Kubernetes vocabulary is about the backend destinations, not the public entrypoint.
Operationally, endpoints are useful for debugging: if a Service isn't routing traffic, checking Endpoints/EndpointSlices shows whether the Service actually has backends and whether readiness is excluding Pods. This ties directly into Kubernetes service discovery and load balancing: the Service is the stable front door; endpoints are the actual backends.

NEW QUESTION # 122
......
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