KCNA TEST PASS4SURE | KCNA FREE DUMPS

KCNA Test Pass4sure | KCNA Free Dumps

KCNA Test Pass4sure | KCNA Free Dumps

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q56-Q61):

NEW QUESTION # 56
Explain the difference between a Docker image and a Docker container. Provide practical scenarios where they are used.

  • A. Docker images are used for storing data, while containers are used for running applications.
  • B. Docker images and containers are the same thing, just different names.
  • C. A Docker container is a blueprint for creating an image, while an image is a running instance of that container.
  • D. A Docker image is a blueprint for creating a container, while a container is a running instance of that image.
  • E. Docker images are used for running applications, while containers are used for storing data.

Answer: D

Explanation:
A Docker image is a static, immutable template that contains all the necessary components (files, libraries, dependencies, etc.) to run a specific application. Think of it as a blueprint. A Docker container is a running instance of that image, meaning it's an actual process running on your system. You can create multiple containers from the same image. Practical Scenarios: Image: You create a Docker image for a web server application, including the web server software, configuration files, and application code. This image can be shared with others or deployed to different environments. Container: You run multiple instances of this web server image as containers on your server. Each container gets its own isolated environment, allowing you to scale your web application easily.


NEW QUESTION # 57
You're running a web application on Kubernetes that experiences occasional traffic spikes. Which of the following strategies is most suitable for managing costs during these spikes without compromising performance?

  • A. Configure a resource quota for the web application's namespace to limit its resource usage.
  • B. Manually scale the deployment up during traffic spikes and down afterward.
  • C. Utilize serverless functions to handle the peak traffic load, automatically scaling as needed.
  • D. Use a horizontal pod autoscaler (HPA) with metrics based on CPU and memory utilization.
  • E. Implement a caching layer in front of the web application to reduce the load on the backend servers.

Answer: D,E

Explanation:
For web applications with unpredictable traffic patterns, dynamic scaling and load optimization are crucial for cost management. Horizontal Pod Autoscaler (HPA) with CPU and memory utilization metrics automatically scales the deployment up and down based on actual load, effectively managing resources during spikes. Implementing a caching layer reduces load on the backend servers, further reducing resource consumption and costs. Manually scaling is inefficient, and resource quotas don't address the dynamic nature of traffic spikes. While serverless functions offer scaling, they might not be suitable for all web application components.


NEW QUESTION # 58
You have a CI/CD pipeline that builds and deploys a new version of your application to Kubernetes. After deployment, you notice performance issues and need to roll back to the previous version. Which of the following strategies can be used to achieve a smooth rollback?

  • A. Manually delete the new pods and restart the old pods.
  • B. Use a canary deployment strategy to gradually roll back the traffic.
  • C. Use Kubernetes Deployments with rollback capabilities to revert to a previous deployment revision.
  • D. Redeploy the previous version of the application manually.
  • E. Edit the Deployment configuration to change the container image to the previous version.

Answer: C

Explanation:
Kubernetes Deployments track historical revisions. By leveraging rollback capabilities within Deployments, you can easily revert to a previous successful deployment revision, ensuring a smooth transition without manual intervention.


NEW QUESTION # 59
You have a Kubernetes cluster with multiple namespaces. You are running different applications in separate namespaces and want to ensure that HPAs in each namespace are not interfering with each other. Which of the following measures should you take?

  • A. Create a custom controller to manage HPAs across multiple namespaces and coordinate scaling decisions.
  • B. There is no need for any specific measures as HPAs are isolated by default within their respective namespaces.
  • C. Configure resource quotas for each namespace to prevent resource over-allocation by HPAs.
  • D. Ensure that the •minReplicas• and •maxReplicaS values for HPAs across namespaces are aligned.
  • E. Use a single HPA for all applications in all namespaces.

Answer: A,C

Explanation:
You need to ensure that HPAs are not competing for resources across namespaces- Resource quotas (C) limit resource consumption within each namespace, preventing HPAs from consuming resources intended for other applications. A custom controller (D) can be used to manage HPAs across multiple namespaces, coordinating scaling decisions to avoid resource contention and optimize utilization across the cluster. While options A and B might seem relevant, they are not effective solutions for preventing interference between HPAs in different namespaces. Option E is incorrect; HPAs operate within their namespaces, but resource contention can occur if not managed properly.


NEW QUESTION # 60
To specify a Kubernetes object which language is used?

  • A. Python
  • B. YAML
  • C. Node
  • D. JSON
  • E. Go

Answer: B

Explanation:
https://kubernetes.io/docs/concepts/overview/working-with-objects/kubernetes-objects/


NEW QUESTION # 61
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