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100% Pass Quiz 2025 Linux Foundation KCNA: Kubernetes and Cloud Native Associate–Efficient New Dumps Ebook

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q33-Q38):

NEW QUESTION # 33
What does the 'kops' acronym means?

A. Kubernetes Open Platform Specification
B. Kubernetes Operators
C. Kubernetes Operation Policy Specification
D. Kubernetes Operations

Answer: D

Explanation:
https://github.com/kubernetes/kops

NEW QUESTION # 34
What is the default service type in Kubernetes?

A. loadBalancer
B. serviceType
C. CusterIP
D. NodePort

Answer: C

Explanation:
https://kubernetes.io/docs/concepts/services-networking/service/#publishing-services-service-types

NEW QUESTION # 35
Which is not a service type in Kubernetes?

A. ClusterIP
B. ExternalName
C. NodePort
D. LoadBalancer
E. Ingress

Answer: E

Explanation:
https://kubernetes.io/docs/tutorials/kubernetes-basics/expose/expose-intro/

NEW QUESTION # 36
You are running a database service in Kubernetes with a HorizontalPodAutoscaler (HPA) configured. The HPA is set to scale based on CPU utilization. However, you notice that the database often experiences spikes in CPU usage that cause the HPA to scale up unnecessarily. How can you refine the HPA configuration to avoid these unnecessary scaling events?

A. Use a combination of options A and D to fine-tune the HPA behavior.
B. Increase the CPU utilization threshold for scaling up to a higher value.
C. Use a custom metric, such as database connection count, to trigger scaling events.
D. Implement a caching layer to reduce the database load.
E. Configure the HPA to scale down the database only when CPU utilization drops below a certain threshold for a specific duration.

Answer: A,B,C,E

Explanation:
Several approaches can be used to refine the HPA configuration. Increasing the CPU utilization threshold (A) prevents scaling for minor spikes. Using a custom metric like database connection count (B) provides a more accurate measure of database load. Configuring the HPA to scale down only after a sustained drop in CPU utilization (D) avoids over-reacting to temporary spikes. Combining these techniques (E) can further refine the HPA behavior.

NEW QUESTION # 37
What is autoscaling?

A. Automatically adding or removing compute resources as needed
B. Automatically repairing broken application instances
C. Automatically assigning workloads to nodes in a cluster
D. Automatically measuring resource usage

Answer: A

Explanation:
https://kubernetes.io/blog/2016/07/autoscaling-in-kubernetes/
Autoscaling means automatically scaling up or down in response to real-time usage data.

NEW QUESTION # 38
......

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