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KCNA Best Vce: Kubernetes and Cloud Native Associate - High Pass-Rate Linux Foundation Certification KCNA Training

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Linux Foundation Kubernetes and Cloud Native Associate (KCNA) Certification Exam is a vendor-neutral exam designed to test an individual's knowledge of Kubernetes and cloud-native technologies. KCNA exam is intended for individuals who are looking to validate their skills in container orchestration and deployment, as well as the broader ecosystem of cloud-native applications and services. The KCNA Certification is an excellent way for IT professionals to demonstrate their expertise in these critical areas and gain a competitive edge in the job market.

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The Linux Foundation KCNA Web-Based Practice Exam

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q135-Q140):

NEW QUESTION # 135
You are running a Kubernetes cluster with multiple nodes. Your application's Pods need to be scheduled across different nodes for high availability. Which Kubernetes concept allows you to control the distribution of Pods across the cluster?

A. Ingress
B. Node Affinity
C. Pod Security Policy
D. Service
E. Deployment

Answer: B

Explanation:
Node Affinity lets you define rules to control where your Pods are scheduled. You can specify hard (required) or soft (preferred) preferences for nodes based on labels or other criteria. This helps to distribute Pods across the cluster for better resource utilization and fault tolerance.

NEW QUESTION # 136
You are running a web application with a high demand for CPU resources. Which Kubernetes scheduling strategy could help you ensure pods are scheduled on nodes with the most available CPU capacity?

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

Answer: A

Explanation:
Node affinity allows you to define preferences for where pods should be scheduled based on node labels. You can use node affinity to prioritize scheduling on nodes with high CPU capacity. While the other options can influence scheduling, they are not directly focused on CPU availability.

NEW QUESTION # 137
You're designing a serverless application that processes large amounts of data using AWS Lambd
a. You need to ensure that your Lambda functions can handle potential spikes in workload without compromising performance. Which of the following approaches would be most effective in achieving this goal?

A. Utilize AWS Elastic Beanstalk to auto-scale Lambda functions based on workload.
B. Use serverless frameworks like AWS Serverless Application Model (SAM) to manage Lambda deployments.
C. Implement a message queue in front of Lambda functions to buffer incoming requests.
D. Configure Lambda functions to use a larger memory allocation.
E. Enable concurrency settings for Lambda functions to allow multiple invocations simultaneously.

Answer: C,E

Explanation:
The most effective approaches to handle workload spikes are implementing a message queue (like SQS or SNS) to buffer requests and enabling concurrency settings on Lambda functions. Option A increases memory but doesn't address scalability. Option C is not relevant to Lambda auto-scaling. Option D is a deployment tool, not directly related to handling workload spikes. Option E directly enables parallel processing, making it a suitable solution.

NEW QUESTION # 138
You are running a Kubernetes cluster with a single node. You need to ensure that your application remains available even if the node fails. Which Kubernetes feature should you use?

A. High availability
B. Rolling updates
C. Horizontal Pod Autoscaling
D. Pod Disruption Budgets
E. Node affinity

Answer: A

Explanation:
High availability in Kubernetes is achieved by running your cluster on multiple nodes. This ensures that your application remains available even if one node fails, as the workloads can be automatically moved to other healthy nodes.

NEW QUESTION # 139
Explain the concept of "histogram" metrics in Prometheus and provide an example of when they would be beneficial for monitoring.

A. Histogram metrics provide a detailed distribution of values for a specific metric, allowing for analysis of performance bottlenecks or outlier behavior.
B. Histogram metrics are only applicable to monitoring Kubernetes clusters, not for general system monitoring.
C. Histogram metrics are used to create custom dashboards with interactive elements for visualizing data.
D. Histogram metrics are designed to track changes in metric values over time, providing a historical view of performance trends.
E. Histogram metrics are used for storing and retrieving time series data in a compressed format, optimizing storage space.

Answer: A

Explanation:
Histogram metrics provide a distribution of values for a metric. This is beneficial for monitoring scenarios where you want to understand the spread of values, such as response times of a web service. You can use the histogram to see how many requests fall within different response time buckets, helping identify performance bottlenecks or outlier requests. Histogram metrics can help identify performance issues that might not be evident with a simple average or sum metric.

NEW QUESTION # 140
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Isabelle Harris Isabelle Harris

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