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[General] New Linux Foundation KCNA Test Cost & Books KCNA PDF

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【General】 New Linux Foundation KCNA Test Cost & Books KCNA PDF

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2026 Latest PassExamDumps KCNA PDF Dumps and KCNA Exam Engine Free Share: https://drive.google.com/open?id=1aL3PK0WCSbXV61elZ07h83jr4thwK4gk
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Books Linux Foundation KCNA PDF & KCNA Reliable Dumps FreeIn today's technological world, more and more students are taking the Linux Foundation KCNA exam online. While this can be a convenient way to take a KCNA exam dumps, it can also be stressful. Luckily, PassExamDumps's best Linux Foundation KCNA Exam Questions can help you prepare for your KCNA certification exam and reduce your stress.
To prepare for the KCNA Exam, candidates can take advantage of a variety of resources offered by the Linux Foundation. These include online courses, study guides, and practice exams. The Linux Foundation also offers a Kubernetes Fundamentals course that covers the basics of Kubernetes and is a good starting point for those new to the technology.
Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q104-Q109):NEW QUESTION # 104
You are using Prometheus to monitor your Kubernetes cluster. You notice that several pods are
experiencing high memory usage. You want to investigate further to determine which containers within these pods are consuming the most memory. How can you effectively use Prometheus to identify these memory-intensive containers?
  • A. Utilize the metric to identify pods that are not ready due to memory constraints.
  • B. Use the 'kube_pod_container_status_memory_usage_bytes' metric to analyze the actual memory usage of each container.
  • C. Utilize the • metric to identify containers that are frequently restarting due to memory pressure.
  • D. Filter Prometheus queries by container name and sort by memory usage to pinpoint the memory-intensive containers.
  • E. Use the metric to identify the requested memory limits for each containa
Answer: B,D
Explanation:
Both options B and D provide effective solutions for identifying memory-intensive containers. Option B allows you to directly analyze the 'kube_pod_container_status_memory_usage_bytes• metric, which provides the actual memory usage of each container within the pod. Option D suggests filtering Prometheus queries by container name and sorting by memory usage, enabling you to easily pinpoint containers with the highest memory consumption. While option A provides information about requested memory limits, it doesn't directly reflect the actual memory usage. Options C and E are not directly relevant to identifying memory-intensive containers.

NEW QUESTION # 105
You are deploying a web application that requires a specific version of Node.js. How would you ensure that the correct Node.js version is installed on all nodes in your Kubernetes cluster?
  • A. Use a Dockerfile to include the specific Node.js version in the container image for the web application.
  • B. Use a Helm chart to manage the installation of Node.js on all nodes in the cluster.
  • C. Install Node.js manually on each node before deploying the application.
  • D. Define a custom Kubernetes resource to manage Node.js installation across the cluster.
  • E. Use a Kubernetes init container to install Node.js before the main application container starts.
Answer: A
Explanation:
The most effective way to ensure the correct Node.js version is used is to include it within the container image using a Dockerfile. By defining the Node.js version in the Dockerfile, you guarantee that every container built from that image will have the required version, eliminating dependency issues across nodes in your cluster. The container image becomes a self-contained unit, ensuring consistency and simplifying deployment.

NEW QUESTION # 106
Which of the following is a definition of Hybrid Cloud?
  • A. A combination of services running in public and private data centers, only including data centers from the same cloud provider.
  • B. A combination of services running in public and private data centers, excluding serverless functions.
  • C. A cloud native architecture that uses services running in public clouds, excluding data centers in different availability zones.
  • D. A cloud native architecture that uses services running in different public and private clouds, including on-premises data centers.
Answer: D
Explanation:
A hybrid cloud architecture combines public cloud and private/on-premises environments, often spanning multiple infrastructure domains while maintaining some level of portability, connectivity, and unified operations. Option C captures the commonly accepted definition: services run across public and private clouds, including on-premises data centers, so C is correct.
Hybrid cloud is not limited to a single cloud provider (which is why A is too restrictive). Many organizations adopt hybrid cloud to meet regulatory requirements, data residency constraints, latency needs, or to preserve existing investments while still using public cloud elasticity. In Kubernetes terms, hybrid strategies often include running clusters both on-prem and in one or more public clouds, then standardizing deployment through Kubernetes APIs, GitOps, and consistent security/observability practices.
Option B is incorrect because excluding data centers in different availability zones is not a defining property; in fact, hybrid deployments commonly use multiple zones/regions for resilience. Option D is a distraction: serverless inclusion or exclusion does not define hybrid cloud. Hybrid is about the combination of infrastructure environments, not a specific compute model.
A practical cloud-native view is that hybrid architectures introduce challenges around identity, networking, policy enforcement, and consistent observability across environments. Kubernetes helps because it provides a consistent control plane API and workload model regardless of where it runs. Tools like service meshes, federated identity, and unified monitoring can further reduce fragmentation.
So, the most accurate definition in the given choices is C: hybrid cloud combines public and private clouds, including on-premises infrastructure, to run services in a coordinated architecture.

NEW QUESTION # 107
Which of the following best describes the concept of 'cold starts' in serverless computing?
  • A. The overhead associated with invoking a serverless function from an external client.
  • B. The time taken for a serverless function to access and process data from a database.
  • C. The time it takes for a serverless function to be initialized and ready to execute after a period of inactivity.
  • D. The time required for a serverless function to scale up to handle increased workload.
  • E. The latency introduced by network communication between a serverless function and its dependencies.
Answer: C
Explanation:
Cold starts refer to the time it takes for a serverless function to be provisioned, loaded into memory, and ready for execution after a period of inactivity. This latency is a characteristic of serverless environments and can affect performance, especially for infrequent function invocations. Options B, C, D, and E describe other factors that can contribute to latency in serverless applications but are not the defining characteristic of cold starts

NEW QUESTION # 108
Which API object is the recommended way to run a scalable, stateless application on your cluster?
  • A. Pod
  • B. Deployment
  • C. DaemonSet
  • D. ReplicaSet
Answer: B
Explanation:
For a scalable, stateless application, Kubernetes recommends using a Deployment because it provides a higher-level, declarative management layer over Pods. A Deployment doesn't just "run replicas"; it manages the entire lifecycle of rolling out new versions, scaling up/down, and recovering from failures by continuously reconciling the current cluster state to the desired state you define. Under the hood, a Deployment typically creates and manages a ReplicaSet, and that ReplicaSet ensures a specified number of Pod replicas are running at all times. This layering is the key: you get ReplicaSet's self-healing replica maintenance plus Deployment's rollout/rollback strategies and revision history.
Why not the other options? A Pod is the smallest deployable unit, but it's not a scalable controller-if a Pod dies, nothing automatically replaces it unless a controller owns it. A ReplicaSet can maintain N replicas, but it does not provide the full rollout orchestration (rolling updates, pause/resume, rollbacks, and revision tracking) that you typically want for stateless apps that ship frequent releases. A DaemonSet is for node-scoped workloads (one Pod per node or subset of nodes), like log shippers or node agents, not for "scale by replicas." For stateless applications, the Deployment model is especially appropriate because individual replicas are interchangeable; the application does not require stable network identities or persistent storage per replica. Kubernetes can freely replace or reschedule Pods to maintain availability. Deployment strategies (like RollingUpdate) allow you to upgrade without downtime by gradually replacing old replicas with new ones while keeping the Service endpoints healthy. That combination-declarative desired state, self-healing, and controlled updates-makes Deployment the recommended object for scalable stateless workloads.

NEW QUESTION # 109
......
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