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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which of the following features is NOT a primary benefit of using a service mesh like Istio in a Kubernetes environment?

A. Dynamic traffic routing and fault tolerance capabilities
B. Improved security through mutual TLS authentication between services
C. Enhanced observability with metrics, tracing, and logging
D. Direct access to the underlying Kubernetes API for granular control
E. Simplified service discovery and load balancing across pods

Answer: D

Explanation:
Service meshes like Istio are designed to abstract away the complexities of managing microservices in Kubernetes, providing a layer of abstraction above the Kubernetes API. While they offer enhanced security, observability, and traffic management, they do not provide direct access to the Kubernetes API. This allows developers to focus on application logic rather than infrastructure management.

NEW QUESTION # 21
You have a Kubernetes cluster with a HorizontalPodAutoscaler (HPA) configured for your application. The HPA is set to scale the application based on CPU utilization. The application suddenly experiences a surge in traffic, causing high CPU usage. How does the HPA respond to this scenario?

A. The HPA scales up the application after a configurable delay to avoid over-provisioning.
B. The HPA immediately scales up the application by creating new Pods.
C. The HPA scales up the application only if CPU utilization exceeds a predefined threshold.
D. The HPA only scales down the application when CPU utilization drops below a certain threshold.
E. The HPA scales up the application by increasing the resource requests for existing Pods.

Answer: C

Explanation:
The HPA typically has a defined threshold (e.g., 80% CPU utilization) and a scaling policy (e.g., add one Pod if CPU exceeds the threshold). The HPA won't immediately scale up upon a traffic surge; it will wait for the CPU utilization to surpass the specified threshold before triggering the scaling action.

NEW QUESTION # 22
You're managing a Kubernetes cluster with several deployments using resource requests and limits. Which of the following strategies can help you effectively manage resource utilization and potentially reduce costs?

A. Set resource limits lower than requests to encourage efficient resource use.
B. Use Horizontal Pod Autoscaler (HPA) to adjust the number of pods based on resource utilization and load.
C. Configure resource reservation to ensure a minimum amount of resources is always available.
D. Increase resource requests for all deployments to ensure enough resources are always available.
E. Utilize resource quotas to restrict the total resources that can be consumed by namespaces.

Answer: B,C,E

Explanation:
The most effective strategies for managing resource utilization and cost in Kubernetes involve dynamic scaling and resource allocation. Horizontal Pod Autoscaler (HPA) allows you to adjust the number of pods based on resource utilization, reducing overprovisioning. Resource quotas restrict the total resources that can be consumed by namespaces, preventing resource exhaustion and potential cost spikes. Resource reservation guarantees a minimum amount of resources for critical applications, ensuring they perform well even under high load. Increasing requests without adjusting limits can lead to overprovisioning, while setting limits lower than requests can limit pod performance.

NEW QUESTION # 23
You are monitoring a Kubernetes cluster using Prometheus. You notice a significant spike in CPUusage on a specific pod. To investigate further, you want to collect metrics related to individual processes running within that pod. Which Prometheus query can effectively achieve this?

Answer: A

Explanation:
The query •process_cpu_seconds_total{pod="", container="", process=""Y is designed to retrieve CPU usage metrics for specific processes running within a container. The other options are not focused on individual process metrics.

NEW QUESTION # 24
In Prometheus, what is the purpose of recording rules and how do they differ from alerting rules?

A. Recording rules are used for data storage optimization, while alerting rules are used for system performance monitoring.
B. Recording rules create new time series based on existing metrics, while alerting rules trigger notifications based on metric conditions.
C. Recording rules are primarily used for troubleshooting, while alerting rules are used for proactive issue detection.
D. Recording rules define data aggregation and analysis, while alerting rules focus on time-series data visualization.
E. Recording rules and alerting rules are interchangeable and can be used for similar purposes.

Answer: B

Explanation:
Recording rules transform existing time series data into new, calculated metrics. Alerting rules, on the other hand, trigger notifications when certain metric conditions are met. Recording rules are used for data pre-processing, while alerting rules focus on proactive monitoring.

NEW QUESTION # 25
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Linux Foundation certification KCNA exam best training materials

Trustworthy KCNA Reliable Braindumps Free | Easy To Study and Pass Exam at first attempt & Well-Prepared Linux Foundation Kubernetes and Cloud Native Associate

Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q20-Q25):

Unique KCNA Learning Guide display the most authentic Exam Questions - ITCertMagic

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