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| Certification Vendor: | Linux Foundation / CNCF |
|---|---|
| Exam Name: | Kubernetes and Cloud Native Associate (KCNA) Exam |
| Exam Number: | KCNA |
| Exam Price: | $250 USD |
| Related Certifications: | CKS (Certified Kubernetes Security Specialist) CKA (Certified Kubernetes Administrator) KCSA (Kubernetes and Cloud Native Security Associate) CKAD (Certified Kubernetes Application Developer) |
| Real Exam Qty: | 60 |
| Certificate Validity Period: | 2 years |
| Passing Score: | 75% |
| Available Languages: | Japanese, English |
| Exam Duration: | 90 minutes |
| Exam Format: | Online proctored, Multiple choice |
| Recommended Training: | LFS250: Kubernetes and Cloud Native Essentials |
| Exam Registration: | Official Registration |
| Sample Questions: | Linux Foundation KCNA Sample Questions |
| Exam Way: | Online, remotely proctored; available worldwide |
| Pre Condition: | No mandatory prerequisites; basic IT knowledge and familiarity with command-line tools recommended |
| Official Syllabus URL: | https://training.linuxfoundation.org/certification/kubernetes-cloud-native-associate/ |
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Linux Foundation KCNA (Kubernetes and Cloud Native Associate) Certification Exam is a globally recognized certification program that validates one's knowledge and skills in Kubernetes and cloud-native technologies. Kubernetes is an open-source container orchestration system that automates the deployment, scaling, and management of containerized applications. Cloud-native technologies, on the other hand, refer to a set of tools and practices that enable the development and deployment of applications in a cloud-native environment.
NEW QUESTION # 17
Which kubernetes component is the smallest deployable unit of computing?
Answer: B
NEW QUESTION # 18
What is a DaemonSet?
Answer: C
Explanation:
A DaemonSet ensures that a copy of a Pod runs on each node (or a selected subset of nodes), which matches option A and makes it correct. DaemonSets are ideal for node-level agents that should exist everywhere, such as log shippers, monitoring agents, CNI components, storage daemons, and security scanners.
DaemonSets differ from Deployments/ReplicaSets because their goal is not "N replicas anywhere," but "one replica per node" (subject to node selection). When nodes are added to the cluster, the DaemonSet controller automatically schedules the DaemonSet Pod onto the new nodes. When nodes are removed, the Pods associated with those nodes are cleaned up. You can restrict placement using node selectors, affinity rules, or tolerations so that only certain nodes run the DaemonSet (for example, only Linux nodes, only GPU nodes, or only nodes with a dedicated label).
Option B sounds like a ReplicaSet/Deployment behavior (stable set of replicas), not a DaemonSet. Option C describes CronJobs (scheduled, recurring run-to-completion workloads). Option D describes StatefulSets, which provide stable identity, ordering, and uniqueness guarantees for stateful replicas.
Operationally, DaemonSets matter because they often run critical cluster services. During maintenance and upgrades, DaemonSet update strategy determines how those node agents roll out across the fleet. Since DaemonSets can tolerate taints (like master/control-plane node taints), they can also be used to ensure essential agents run across all nodes, including special pools. Thus, the correct definition is A.
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NEW QUESTION # 19
Which tools enable Kubernetes HorizontalPodAutoscalers to use custom, application-generated metrics to trigger scaling events?
Answer: A
Explanation:
To scale on custom, application-generated metrics, the Horizontal Pod Autoscaler (HPA) needs those metrics exposed through the Kubernetes custom metrics (or external metrics) API. A common and Kubernetes-documented approach is Prometheus + prometheus-adapter, making A correct. Prometheus scrapes application metrics (for example, request rate, queue depth, in-flight requests) from /metrics endpoints. The prometheus-adapter then translates selected Prometheus time series into the Kubernetes Custom Metrics API so the HPA controller can fetch them and make scaling decisions.
Why not the other options: Grafana is a visualization tool; it does not provide the metrics API translation layer required by HPA, so "Grafana and Prometheus" is incomplete. Graylog is primarily a log management system; it's not the standard solution for feeding custom metrics into HPA via the Kubernetes metrics APIs. The "kubernetes-adapter" term in option C is not the standard named adapter used in the common Kubernetes ecosystem for Prometheus-backed custom metrics (the recognized component is prometheus-adapter).
This matters operationally because HPA is not limited to CPU/memory. CPU and memory use resource metrics (often from metrics-server), but modern autoscaling often needs application signals: message queue length, requests per second, latency, or business metrics. With Prometheus and prometheus-adapter, you can define HPA rules such as "scale to maintain queue depth under X" or "scale based on requests per second per pod." This can produce better scaling behavior than CPU-based scaling alone, especially for I/O-bound services or workloads with uneven CPU profiles.
So the correct tooling combination in the provided choices is Prometheus and the prometheus-adapter, option A.
NEW QUESTION # 20
In a Kubernetes cluster, which component is responsible for maintaining the desired state of applications by reconciling resources such as Deployments and ReplicaSets?
Answer: D
Explanation:
The Controller Manager runs controllers that continuously compare the current state of resources with the desired state and take corrective actions to reconcile differences, ensuring applications remain in the intended state.
NEW QUESTION # 21
Which component of the node is responsible to run workloads?
Answer: A
Explanation:
The verified correct answer is D (the container runtime). On a Kubernetes node, the container runtime (such as containerd or CRI-O) is the component that actually executes containers-it creates container processes, manages their lifecycle, pulls images, and interacts with the underlying OS primitives (namespaces, cgroups) through an OCI runtime like runc. In that direct sense, the runtime is what "runs workloads." It's important to distinguish responsibilities. The kubelet (A) is the node agent that orchestrates what should run on the node: it watches the API server for Pods assigned to the node and then asks the runtime to start/stop containers accordingly. Kubelet is essential for node management, but it does not itself execute containers; it delegates execution to the runtime via CRI. kube-proxy (B) handles Service traffic routing rules (or is replaced by other dataplanes) and does not run containers. kube-apiserver (C) is a control plane component that stores and serves cluster state; it is not a node workload runner.
So, in the execution chain: scheduler assigns Pod → kubelet sees Pod assigned → kubelet calls runtime via CRI → runtime launches containers. When troubleshooting "containers won't start," you often inspect kubelet logs and runtime logs because the runtime is the component that can fail image pulls, sandbox creation, or container start operations.
Therefore, the best answer to "which node component is responsible to run workloads" is the container runtime, option D.
NEW QUESTION # 22
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