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Linux Foundation KCNA (Kubernetes and Cloud Native Associate) Exam is a certification program designed to test one's knowledge and skills in the field of Kubernetes and Cloud Native technologies. Kubernetes and Cloud Native Associate certification is globally recognized and is a valuable asset for professionals looking to advance their careers in the cloud computing industry. KCNA Exam covers a wide range of topics including Kubernetes architecture, deployment, networking, security, and troubleshooting. It also covers cloud-native technologies such as containerization, microservices, and serverless computing.
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Linux Foundation KCNA Exam is an ideal certification for professionals who are interested in working with cloud-native technologies or who are already working in the field. Kubernetes and Cloud Native Associate certification can help individuals advance their careers and increase their earning potential by demonstrating their expertise in this rapidly growing field. With the increasing adoption of cloud-native technologies by organizations of all sizes, the demand for professionals with KCNA Certification is expected to continue to grow in the coming years.
Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q92-Q97):
NEW QUESTION # 92
You are running a stateful application on Kubernetes that requires persistent data storage. Which of the following Kubernetes storage classes would be most suitable for this scenario?
Answer: E
Explanation:
Persistent Volumes (PVs) are the most suitable storage class for stateful applications- They guarantee persistent data storage across pod restarts and provide a consistent interface for accessing the data
NEW QUESTION # 93
What is Open Container Initiative 'OCI'?
Answer: B
Explanation:
https://opencontainers.org/
NEW QUESTION # 94
You are running a large-scale, microservice-based application in Kubernetes, and you are using HorizontalPodAutoscalers (HPAs) for automatic scaling. Which of the following statements about resource utilization monitoring in this scenario is true?
Answer: E
Explanation:
Monitoring resource utilization at both the Pod and namespace levels provides a comprehensive understanding of resource usage. Monitoring Pods allows you to identify resource-intensive services and fine-tune their scaling behavior. Monitoring namespaces helps you understand the overall resource consumption and optimize scaling across the entire application. This is crucial in large-scale microservice deployments where resources can be easily exhausted if not carefully monitored
NEW QUESTION # 95
You are running a microservice-based application in Kubernetes. Each service has its own HPA configured for scaling. How can you ensure that the scaling decisions for different services are coordinated to prevent resource contention?
Answer: C,D
Explanation:
Both resource quotas and a custom controller can help prevent resource contention. Resource quotas limit the resources each service can consume, preventing one service from monopolizing resources- A custom controller can monitor resource usage across all services and make coordinated scaling decisions to avoid over-provisioning and resource exhaustion.
NEW QUESTION # 96
You have a Kubernetes cluster with three worker nodes. Nodel has 8 CPU cores, Node2 has 4 CPU cores, and Node3 has 2 CPU cores. You deploy a pod with a resource request of 2 CPU cores. What is the likely order in which Kubernetes will attempt to schedule this pod on the available nodes?
Answer: D
Explanation:
Kubernetes typically attempts to schedule pods on nodes that have the most available resources to meet the pod's requests. In this scenario, Nodel has the highest CPU capacity, followed by Node2, and then Node3. The scheduler prioritizes nodes with more available resources to ensure optimal utilization and minimize potential resource contention among pods.
NEW QUESTION # 97
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