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Crack Your Exam with DumpsQuestion KCNA Kubernetes and Cloud Native Associate Practice QuestionsDumpsQuestion has created a real Kubernetes and Cloud Native Associate, KCNA exam questions in three forms: Linux Foundation KCNA pdf questions file is the first form. The second and third formats are Web-based and desktop Linux Foundation KCNA practice test software. KCNA pdf dumps file will help you to immediately prepare well for the actual Linux Foundation Kubernetes and Cloud Native Associate. You can download and open the Linux Foundation PDF Questions file anywhere or at any time. KCNA Dumps will work on your laptop, tablet, smartphone, or any other device. You will get a list of actual Linux Foundation KCNA test questions in Linux Foundation KCNA pdf dumps file. Practicing with Web-based and desktop KCNA practice test software you will find your knowledge gap. Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q27-Q32):NEW QUESTION # 27
Which of these events will cause the kube-scheduler to assign a Pod to a node?
A. When the CPU load on the node becomes too high.
B. When the Pod crashes because of an error.
C. When a new node is added to the Kubernetes cluster.
D. When a new Pod is created and has no assigned node.
Answer: D
Explanation:
The kube-scheduler assigns a node to a Pod when the Pod is unscheduled-meaning it exists in the API server but has no spec.nodeName set. The event that triggers scheduling is therefore: a new Pod is created and has no assigned node, which is option D.
Kubernetes scheduling is declarative and event-driven. The scheduler continuously watches for Pods that are in a "Pending" unscheduled state. When it sees one, it runs a scheduling cycle: filtering nodes that cannot run the Pod (insufficient resources based on requests, taints/tolerations, node selectors/affinity rules, topology spread constraints), then scoring the remaining feasible nodes to pick the best candidate. Once selected, the scheduler "binds" the Pod to that node by updating the Pod's spec.nodeName. After that, kubelet on the chosen node takes over to pull images and start containers.
Option A (Pod crashes) does not directly cause scheduling. If a container crashes, kubelet may restart it on the same node according to restart policy. If the Pod itself is replaced (e.g., by a controller like a Deployment creating a new Pod), that new Pod will be scheduled because it's unscheduled-but the crash event itself isn't the scheduler's trigger. Option B (new node added) might create more capacity and affect future scheduling decisions, but it does not by itself trigger assigning a particular Pod; scheduling still happens because there are unscheduled Pods. Option C (CPU load high) is not a scheduling trigger; scheduling is based on declared requests and constraints, not instantaneous node CPU load (that's a common misconception).
So the correct, Kubernetes-architecture answer is D: kube-scheduler assigns nodes to Pods that are newly created (or otherwise pending) and have no assigned node.
NEW QUESTION # 28
Which mechanism can be used to automatically adjust the amount of resources for an application?
A. Kubernetes Event-driven Autoscaling (KEDA)
B. Cluster Autoscaler
C. Vertical Pod Autoscaler (VPA)
D. Horizontal Pod Autoscaler (HPA)
Answer: D
Explanation:
The verified answer in the PDF is A (HPA), and that aligns with the common Kubernetes meaning of "adjust resources for an application" by scaling replicas. The Horizontal Pod Autoscaler automatically changes the number of Pod replicas for a workload (typically a Deployment) based on observed metrics such as CPU utilization, memory (in some configurations), or custom/external metrics. By increasing replicas under load, the application gains more total CPU/memory capacity available across Pods; by decreasing replicas when load drops, it reduces resource consumption and cost.
It's important to distinguish what each mechanism adjusts:
HPA adjusts replica count (horizontal scaling).
VPA adjusts Pod resource requests/limits (vertical scaling), which is literally "amount of CPU/memory per pod," but it often requires restarts to apply changes depending on mode.
Cluster Autoscaler adjusts the number of nodes in the cluster, not application replicas.
KEDA is event-driven autoscaling that often drives HPA behavior using external event sources (queues, streams), but it's not the primary built-in mechanism referenced in many foundational Kubernetes questions.
Given the wording and the provided answer key, the intended interpretation is: "automatically adjust the resources available to the application" by scaling out/in the number of replicas. That's exactly HPA's role. For example, if CPU utilization exceeds a target (say 60%), HPA computes a higher desired replica count and updates the workload. The Deployment then creates more Pods, distributing load and increasing available compute.
So, within this question set, the verified correct choice is A (Horizontal Pod Autoscaler).
NEW QUESTION # 29
What are the two steps performed by the kube-scheduler to select a node to schedule a pod?
A. Grouping and placing
B. Scoring and creating
C. Filtering and scoring
D. Filtering and selecting
Answer: C
Explanation:
The kube-scheduler selects a node in two main phases: filtering and scoring, so C is correct. First, filtering identifies which nodes are feasible for the Pod by applying hard constraints. These include resource availability (CPU/memory requests), node taints/tolerations, node selectors and required affinities, topology constraints, and other scheduling requirements. Nodes that cannot satisfy the Pod's requirements are removed from consideration.
Second, scoring ranks the remaining feasible nodes using priority functions to choose the "best" placement. Scoring can consider factors like spreading Pods across nodes/zones, packing efficiency, affinity preferences, and other policies configured in the scheduler. The node with the highest score is selected (with tie-breaking), and the scheduler binds the Pod by setting spec.nodeName.
Option B ("filtering and selecting") is close but misses the explicit scoring step that is central to scheduler design. The scheduler does "select" a node, but the canonical two-step wording in Kubernetes scheduling is filtering then scoring. Options A and D are not how scheduler internals are described.
Operationally, understanding filtering vs scoring helps troubleshoot scheduling failures. If a Pod can't be scheduled, it failed in filtering-kubectl describe pod often shows "0/... nodes are available" reasons (insufficient CPU, taints, affinity mismatch). If it schedules but lands in unexpected places, it's often about scoring preferences (affinity weights, topology spread preferences, default scheduler profiles).
So the verified correct answer is C: kube-scheduler uses Filtering and Scoring.
NEW QUESTION # 30
Which kubectl command is useful for collecting information about any type of resource that is active in a Kubernetes cluster?
A. describe
B. explain
C. list
D. expose
Answer: A
Explanation:
The correct answer is A (describe), used as kubectl describe <resource> <name>. kubectl describe is a troubleshooting-focused command that provides a rich, human-readable view of a specific live object in the cluster, including key fields, status, and-crucially-Events related to that object. This makes it extremely useful for "collecting information" about almost any active resource: Pods, Deployments, Nodes, Services, PersistentVolumeClaims, and more.
kubectl get (not listed) is typically used for listing objects and their summary fields, but kubectl describe goes deeper: for a Pod it will show container images, resource requests/limits, probes, mounted volumes, node assignment, IPs, conditions, and recent scheduling/pulling/starting events. For a Node it shows capacity/allocatable resources, labels/taints, conditions, and node events. Those event details often explain why something is Pending, failing to pull images, failing readiness checks, or being evicted.
Option B ("list") is not a standard kubectl subcommand for retrieving resource information (you would use get for listing). Option C (expose) is for creating a Service to expose a resource (like a Deployment). Option D (explain) is for viewing API schema/field documentation (e.g., kubectl explain deployment.spec.replicas) and does not report what is currently happening in the cluster.
So, for gathering detailed live diagnostics about a resource in the cluster, the best kubectl command is kubectl describe, which corresponds to option A.
NEW QUESTION # 31
If kubectl is failing to retrieve information from the cluster, where can you find Pod logs to troubleshoot?
A. /var/log/k8s/
B. /var/log/pods/
C. /etc/kubernetes/
D. ~/.kube/config
Answer: B
Explanation:
The correct answer is A: /var/log/pods/. When kubectl logs can't retrieve logs (for example, API connectivity issues, auth problems, or kubelet/API proxy issues), you can often troubleshoot directly on the node where the Pod ran. Kubernetes nodes typically store container logs on disk, and a common location is under /var/log/pods/, organized by namespace, Pod name/UID, and container. This directory contains symlinks or files that map to the underlying container runtime log location (often under /var/log/containers/ as well, depending on distro/runtime setup).
Option B (~/.kube/config) is your local kubeconfig file; it contains cluster endpoints and credentials, not Pod logs. Option D (/etc/kubernetes/) contains Kubernetes component configuration/manifests on some installations (especially control plane), not application logs. Option C (/var/log/k8s/) is not a standard Kubernetes log path.
Operationally, the node-level log locations depend on the container runtime and logging configuration, but the Kubernetes convention is that kubelet writes container logs to a known location and exposes them through the API so kubectl logs works. If the API path is broken, node access becomes your fallback. This is also why secure node access is sensitive: anyone with node root access can potentially read logs (and other data), which is part of the threat model.
So, the best answer for where to look on the node for Pod logs when kubectl can't retrieve them is /var/log/pods/, option A.
NEW QUESTION # 32
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