LINUX FOUNDATION KCNA EXAM TUTORIAL & NEW KCNA EXAM DURATION

Linux Foundation KCNA Exam Tutorial & New KCNA Exam Duration

Linux Foundation KCNA Exam Tutorial & New KCNA Exam Duration

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Tags: KCNA Exam Tutorial, New KCNA Exam Duration, KCNA PDF Questions, KCNA PDF VCE, KCNA Reliable Test Camp

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Quiz KCNA - Authoritative Kubernetes and Cloud Native Associate Exam Tutorial

Solutions is committed to ace your Linux Foundation KCNA exam preparation and enable you to pass the final KCNA exam with flying colors. To achieve this objective Exams. Solutions is offering updated, real, and error-Free KCNA Exam Questions in three easy-to-use and compatible formats. These KCNA exam questions formats will help you in preparation.

Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q145-Q150):

NEW QUESTION # 145
You are using Kubernetes for deploying a microservices architecture. Each microservice has a unique configuration that needs to be injected into the container at runtime. Which approach is the most effective for managing these dynamic configuration changes?

  • A. Using environment variables during pod creation
  • B. Implementing a custom configuration management system outside of Kubernetes
  • C. Hardcoding configurations in the container image
  • D. Using a service mesh to manage configuration and traffic routing
  • E. Creating a separate ConfigMap for each service and mounting it as a volume

Answer: D

Explanation:
A service mesh like Istio provides a centralized and powerful approach to manage configuration changes, traffic routing, and other aspects of your microservices architecture. It simplifies dynamic configuration updates by allowing you to easily modify configurations for individual services without needing to update container images or restart pods.


NEW QUESTION # 146
Your application relies on a backend database service. Using Istio, you want to configure a circuit breaker pattern to prevent cascading failures if the database becomes unresponsive. How would you implement this?

  • A. Use the 'timeout' setting in the Istio configuration to limit the duration of requests to the database
  • B. Utilize Istio's 'fault injection' feature to simulate failures and test the circuit breaker
  • C. Deploy a separate health check pod to monitor the database service and trigger the circuit breaker if needed
  • D. Create a custom Istio VirtualService with a 'destinationRule' specifying a fallback service in case of failure
  • E. Configure the 'retry' policy in the Istio configuration to automatically retry failed requests

Answer: D

Explanation:
Istio's VirtualService and DestinationRule features allow you to configure fallback services. In the case of the database being unavailable, you can define a fallback service or mechanism to handle the request. Option B helps test circuit breaker behavior but doesn't implement it. Option C could be used for retries, but doesn't address the circuit breaker pattern. Option D is related to request timeouts, not circuit breaking. Option E is a manual approach, while Istio provides a more integrated solution.


NEW QUESTION # 147
You are deploying a pod that requires access to a specific storage volume attached to a particular node. Which Kubernetes feature can you utilize to guarantee the pod is scheduled only on that specific node?

  • A. Pod anti-affinity
  • B. Node anti-affinity
  • C. Node affinity
  • D. Pod affinity
  • E. Taints and tolerations

Answer: C,E

Explanation:
You can achieve this by using either •nodeAffinity• or •taints and tolerationS: 'nodeAffinity•• Similar to the previous question, define 'requiredDuringSchedulinglgnoredDuringExecution• to enforce scheduling on the specific node. 'Taints and TolerationS: Apply a taint on the specific node that reflects the volume availability. Then, configure the pod to tolerate that specific taint, ensuring it can only be scheduled on the node with the matching taint. While •podAffinity• and •podAntiAffinity' are useful for grouping or distributing pods, they do not directly guarantee scheduling on a specific node based on volume availability.


NEW QUESTION # 148
You're running a stateful application in Kubernetes with a HorizontalPodAutoscaler (HPA) configured. You want to ensure that scaling does not disrupt the application's state. What approach would you take to address this concern?

  • A. Use a StatefulSet to manage the deployment of the application.
  • B. Use a persistent volume claim (PVC) to store the application's state.
  • C. Configure the HPA to scale up and down only when the application is idle.
  • D. Implement a custom scaling logic that ensures state persistence during scaling events.
  • E. Disable scaling for the application to maintain state consistency.

Answer: A,B,D

Explanation:
Several approaches can help maintain state during scaling for stateful applications: StatefulSets (A) manage stateful applications by providing persistent storage and stable network identities for Pods- Custom scaling logic (C) allows you to implement specific scaling strategies that preserve state, such as graceful shutdowns and data synchronization. Persistent volume claims (PVCs) (D) ensure that the application's data is stored on persistent volumes, preserving it even during scaling events. While options B and E might seem appealing, they are not practical solutions. Scaling during idle periods might not cover all scenarios, and disabling scaling altogether eliminates the benefits of autoscaling.


NEW QUESTION # 149
Consider the following Kubernetes resource configuration for a deployment:

If each pod is consistently utilizing only 50% of the requested CPU and memory, what are the potential cost-saving measures you could take without impacting performance?

  • A. Utilize a vertical pod autoscaler (VPA) to dynamically adjust the resource requests and limits based on actual usage.
  • B. Reduce the 'replicas' value to 2 to decrease the number of pods.
  • C. Reduce the 'requests' values for CPU and memory to 50m and 128Mi, respectively.
  • D. Leave the current configuration as it is; the application is already optimized.
  • E. Increase the 'limits' values for CPU and memory to 400m and 1024Mi, respectively.

Answer: A,C

Explanation:
The application is overprovisioned as pods are only utilizing 50% of requested resources. Reducing the 'requests' values to reflect actual usage (50m CPU and 128Mi memory) would optimize resource allocation without affecting performance. A VPA can further refine the resource allocation dynamically based on actual usage, ensuring optimal resource utilization. Increasing 'limits' would not reduce costs and might even increase them. Reducing replicas might impact performance, while leaving the configuration unchanged would continue overprovisioning and waste resources.


NEW QUESTION # 150
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