KCNA Questions Prepare with Learning Information! 2026 Regularly updated
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NEW QUESTION # 39
What kubectl command is used to retrieve the resource consumption (CPU and memory) for nodes or Pods?
- A. kubectl cluster-info
- B. kubectl version
- C. kubectl api-resources
- D. kubectl top
Answer: D
Explanation:
To retrieve CPU and memory consumption for nodes or Pods, you use kubectl top, so C is correct. kubectl top nodes shows per-node resource usage, and kubectl top pods shows per-Pod (and optionally per-container) usage. This data comes from the Kubernetes resource metrics pipeline, most commonly metrics-server, which scrapes kubelet/cAdvisor stats and exposes them via the metrics.k8s.io API.
It's important to recognize that kubectl top provides current resource usage snapshots, not long-term historical trending. For long-term metrics and alerting, clusters typically use Prometheus and related tooling.
But for quick operational checks-"Is this Pod CPU-bound?" "Are nodes near memory saturation?"-kubectl top is the built-in day-to-day tool.
Option A (kubectl cluster-info) shows general cluster endpoints and info about control plane services, not resource usage. Option B (kubectl version) prints client/server version info. Option D (kubectl api-resources) lists resource types available in the cluster. None of those report CPU/memory usage.
In observability practice, kubectl top is often used during incidents to correlate symptoms with resource pressure. For example, if a node is high on memory, you might see Pods being OOMKilled or the kubelet evicting Pods under pressure. Similarly, sustained high CPU utilization might explain latency spikes or throttling if limits are set. Note that kubectl top requires metrics-server (or an equivalent provider) to be installed and functioning; otherwise it may return errors like "metrics not available." So, the correct command for retrieving node/Pod CPU and memory usage is kubectl top.
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NEW QUESTION # 40
What helps an organization to deliver software more securely at a higher velocity?
- A. CI/CD Pipeline
- B. apt-get
- C. Docker Images
- D. Kubernetes
Answer: A
Explanation:
A CI/CD pipeline is a core practice/tooling approach that enables organizations to deliver software faster and more securely, so D is correct. CI (Continuous Integration) automates building and testing code changes frequently, reducing integration risk and catching defects early. CD (Continuous Delivery/Deployment) automates releasing validated builds into environments using consistent, repeatable steps-reducing manual errors and enabling rapid iteration.
Security improves because automation enables standardized checks on every change: static analysis, dependency scanning, container image scanning, policy validation, and signing/verification steps can be integrated into the pipeline. Instead of relying on ad-hoc human processes, security controls become repeatable gates. In Kubernetes environments, pipelines commonly build container images, run tests, publish artifacts to registries, and then deploy via manifests, Helm, or GitOps controllers-keeping deployments consistent and auditable.
Option A (Kubernetes) is a platform that helps run and manage workloads, but by itself it doesn't guarantee secure high-velocity delivery. It provides primitives (rollouts, declarative config, RBAC), yet the delivery workflow still needs automation. Option B (apt-get) is a package manager for Debian-based systems and is not a delivery pipeline. Option C (Docker Images) are artifacts; they improve portability and repeatability, but they don't provide the end-to-end automation of building, testing, promoting, and deploying across environments.
In cloud-native application delivery, the pipeline is the "engine" that turns code changes into safe production releases. Combined with Kubernetes' declarative deployment model (Deployments, rolling updates, health probes), a CI/CD pipeline supports frequent releases with controlled rollouts, fast rollback, and strong auditability. That is exactly what the question is targeting. Therefore, the verified answer is D.
NEW QUESTION # 41
What Kubernetes control plane component exposes the programmatic interface used to create, manage and interact with the Kubernetes objects?
- A. etcd
- B. kube-controller-manager
- C. kube-apiserver
- D. kube-proxy
Answer: C
Explanation:
The kube-apiserver is the front door of the Kubernetes control plane and exposes the programmatic interface used to create, read, update, delete, and watch Kubernetes objects-so C is correct. Every interaction with cluster state ultimately goes through the Kubernetes API. Tools like kubectl, client libraries, GitOps controllers, operators, and core control plane components (scheduler and controllers) all communicate with the API server to submit desired state and to observe current state.
The API server is responsible for handling authentication (who are you?), authorization (what are you allowed to do?), and admission control (should this request be allowed and possibly mutated/validated?). After a request passes these gates, the API server persists the object's desired state to etcd (the backing datastore) and returns a response. The API server also provides a watch mechanism so controllers can react to changes efficiently, enabling Kubernetes' reconciliation model.
It's important to distinguish this from the other options. etcd stores cluster data but does not expose the cluster's primary user-facing API; it's an internal datastore. kube-controller-manager runs control loops (controllers) that continuously reconcile resources (like Deployments, Nodes, Jobs) but it consumes the API rather than exposing it. kube-proxy is a node-level component implementing Service networking rules and is unrelated to the control-plane API endpoint.
Because Kubernetes is "API-driven," the kube-apiserver is central: if it is unavailable, you cannot create workloads, update configurations, or even reliably observe cluster state. This is why high availability architectures prioritize multiple API server instances behind a load balancer, and why securing the API server (RBAC, TLS, audit) is a primary operational concern.
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NEW QUESTION # 42
Which of the following options is true about considerations for large Kubernetes clusters?
- A. Kubernetes supports up to 50 nodes and recommends no more than 1000 containers per node.
- B. Kubernetes supports up to 1000 nodes and recommends no more than 1000 containers per node.
- C. Kubernetes supports up to 5000 nodes and recommends no more than 500 Pods per node.
- D. Kubernetes supports up to 5000 nodes and recommends no more than 110 Pods per node.
Answer: D
Explanation:
The correct answer is C: Kubernetes scalability guidance commonly cites support up to 5000 nodes and recommends no more than 110 Pods per node. The "110 Pods per node" recommendation is a practical limit based on kubelet, networking, and IP addressing constraints, as well as performance characteristics for scheduling, service routing, and node-level resource management. It is also historically aligned with common CNI/IPAM defaults where node Pod CIDRs are sized for ~110 usable Pod IPs.
Why the other options are incorrect: A and D reference "containers per node," which is not the standard sizing guidance (Kubernetes typically discusses Pods per node). B's "500 Pods per node" is far above typical recommended limits for many environments and would stress IPAM, kubelet, and node resources significantly.
In large clusters, several considerations matter beyond the headline limits: API server and etcd performance, watch/list traffic, controller reconciliation load, CoreDNS scaling, and metrics/observability overhead. You must also plan for IP addressing (cluster CIDR sizing), node sizes (CPU/memory), and autoscaling behavior. On each node, kubelet and the container runtime must handle churn (starts/stops), logging, and volume operations. Networking implementations (kube-proxy, eBPF dataplanes) also have scaling characteristics.
Kubernetes provides patterns to keep systems stable at scale: request/limit discipline, Pod disruption budgets, topology spread constraints, namespaces and quotas, and careful observability sampling. But the exam-style fact this question targets is the published scalability figure and per-node Pod recommendation.
Therefore, the verified true statement among the options is C.
NEW QUESTION # 43
What is the resource type used to package sets of containers for scheduling in a cluster?
- A. Deployment
- B. ReplicaSet
- C. Pod
- D. ContainerSet
Answer: C
Explanation:
The Kubernetes resource used to package one or more containers into a schedulable unit is the Pod, so A is correct. Kubernetes schedules Pods onto nodes; it does not schedule individual containers. A Pod represents a single "instance" of an application component and includes one or more containers that share key runtime properties, including the same network namespace (same IP and port space) and the ability to share volumes.
Pods enable common patterns beyond "one container per Pod." For example, a Pod may include a main application container plus a sidecar container for logging, proxying, or configuration reload. Because these containers share localhost networking and volume mounts, they can coordinate efficiently without requiring external service calls. Kubernetes manages the Pod lifecycle as a unit: the containers in a Pod are started according to container lifecycle rules and are co-located on the same node.
Option B (ContainerSet) is not a standard Kubernetes workload resource. Option C (ReplicaSet) manages a set of Pod replicas, ensuring a desired count is running, but it is not the packaging unit itself. Option D (Deployment) is a higher-level controller that manages ReplicaSets and provides rollout/rollback behavior, again operating on Pods rather than being the container-packaging unit.
From the scheduling perspective, the PodSpec defines container images, commands, resources, volumes, security context, and placement constraints. The scheduler evaluates these constraints and assigns the Pod to a node. This "Pod as the atomic scheduling unit" is fundamental to Kubernetes architecture and explains why Kubernetes-native concepts (Services, selectors, readiness, autoscaling) all revolve around Pods.
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NEW QUESTION # 44
How is application data maintained in containers?
- A. Store data in separate folders.
- B. Store data into sidecar containers.
- C. Store data into data folders.
- D. Store data into volumes.
Answer: D
Explanation:
Container filesystems are ephemeral: the writable layer is tied to the container lifecycle and can be lost when containers are recreated. Therefore, maintaining application data correctly means storing it in volumes, making D the correct answer. In Kubernetes, volumes provide durable or shareable storage that is mounted into containers at specific paths. Depending on the volume type, the data can persist across container restarts and even Pod rescheduling.
Kubernetes supports many volume patterns. For transient scratch data you might use emptyDir (ephemeral for the Pod's lifetime). For durable state, you typically use PersistentVolumes consumed by PersistentVolumeClaims (PVCs), backed by storage systems via CSI drivers (cloud disks, SAN/NAS, distributed storage). This decouples the application container image from its state and enables rolling updates, rescheduling, and scaling without losing data.
Options A and B ("folders") are incomplete because folders inside the container filesystem do not guarantee persistence. A folder is only as durable as the underlying storage; without a mounted volume, it lives in the container's writable layer and will disappear when the container is replaced. Option C is incorrect because
"sidecar containers" are not a data durability mechanism; sidecars can help ship logs or sync data, but persistent data should still be stored on volumes (or external services like managed databases).
From an application delivery standpoint, the principle is: containers should be immutable and disposable, and state should be externalized. Volumes (and external managed services) make this possible. In Kubernetes, this is a foundational pattern enabling safe rollouts, self-healing, and portability: the platform can kill and recreate Pods freely because data is maintained independently via volumes.
Therefore, the verified correct choice is D: Store data into volumes.
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NEW QUESTION # 45
What is the API that exposes resource metrics from the metrics-server?
- A. resources.k8s.io
- B. metrics.k8s.io
- C. cadvisor.k8s.io
- D. custom.k8s.io
Answer: B
Explanation:
The correct answer is C: metrics.k8s.io. Kubernetes' metrics-server is the standard component that provides resource metrics (primarily CPU and memory) for nodes and pods. It aggregates this information (sourced from kubelet/cAdvisor) and serves it through the Kubernetes aggregated API under the group metrics.k8s.io.
This is what enables commands like kubectl top nodes and kubectl top pods, and it is also a key data source for autoscaling with the Horizontal Pod Autoscaler (HPA) when scaling on CPU/memory utilization.
Why the other options are wrong:
* custom.k8s.io is not the standard API group for metrics-server resource metrics. Custom metrics are typically served through the custom metrics API (commonly custom.metrics.k8s.io) via adapters (e.g., Prometheus Adapter), not metrics-server.
* resources.k8s.io is not the metrics-server API group.
* cadvisor.k8s.io is not exposed as a Kubernetes aggregated metrics API. cAdvisor is a component integrated into kubelet that provides container stats, but metrics-server is the thing that exposes the aggregated Kubernetes metrics API, and the canonical group is metrics.k8s.io.
Operationally, it's important to understand the boundary: metrics-server provides basic resource metrics suitable for core autoscaling and "top" views, but it is not a full observability system (it does not store long- term metrics history like Prometheus). For richer metrics (SLOs, application metrics, long-term trending), teams typically deploy Prometheus or a managed monitoring backend. Still, when the question asks specifically which API exposes metrics-server data, the answer is definitively metrics.k8s.io.
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NEW QUESTION # 46
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. Use the metric to identify the requested memory limits for each containa
- 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 pods that are not ready due to memory constraints.
- D. Utilize the • metric to identify containers that are frequently restarting due to memory pressure.
- E. Filter Prometheus queries by container name and sort by memory usage to pinpoint the memory-intensive containers.
Answer: B,E
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 # 47
You are managing a large Kubernetes cluster with multiple namespaces. You want to control access to resources within different namespaces. Which of the following mechanisms can be used to achieve fine-grained access control?
- A. Role-Based Access Control (RBAC)
- B. Network Segmentation
- C. Pod Security Policies
- D. Service Accounts
- E. Network policies
Answer: A,C
Explanation:
Both Role-Based Access Control (RBAC) and Pod Security Policies (PSP) are used for managing access to resources within Kubernetes. RBAC provides fine-grained permissions based on roles and users, while PSPs define security constraints for Pods, limiting their capabilities and access to resources.
NEW QUESTION # 48
A platform engineer is tasked with ensuring that an application can securely access the Kubernetes API without using a developer's personal credentials. What is the correct way to configure this?
- A. Create a ServiceAccount and bind it to the Pod for API access.
- B. Use a developer's kubeconfig file with restricted permissions.
- C. Set the application to use the default ServiceAccount in the namespace.
- D. Generate a certificate for the application to access the API.
Answer: A
Explanation:
In Kubernetes, applications that need to interact with the Kubernetes API should never use a developer's personal credentials. Instead, Kubernetes provides a built-in, secure, and auditable mechanism for workload authentication and authorization using ServiceAccounts. Creating a dedicated ServiceAccount and binding it to the Pod is the correct and recommended approach, making option A the correct answer.
A ServiceAccount represents an identity for processes running inside Pods. When a Pod is configured to use a specific ServiceAccount, Kubernetes automatically injects a short-lived authentication token into the Pod.
This token is securely mounted and can be used by the application to authenticate to the Kubernetes API server. Access to API resources is then controlled using RBAC (Role-Based Access Control) by binding roles or cluster roles to the ServiceAccount, ensuring the application has only the permissions it needs- following the principle of least privilege.
Option B is incorrect because manually generating certificates for application access is not the standard or recommended method for in-cluster authentication. Kubernetes manages ServiceAccount tokens automatically and rotates them as needed, providing a simpler and more secure solution. Option C is incorrect because using a developer's kubeconfig file inside an application introduces serious security risks and violates best practices by coupling workloads to personal credentials. Option D is also incorrect because relying on the default ServiceAccount is discouraged; it often has no permissions or, in some cases, broader permissions than intended. Creating a dedicated ServiceAccount provides clearer security boundaries and auditability.
Using ServiceAccounts integrates cleanly with Kubernetes' authentication and authorization model and is explicitly designed for applications and controllers running inside the cluster. This approach ensures secure API access, centralized permission management, and operational consistency across environments.
Therefore, the correct and verified answer is Option A: Create a ServiceAccount and bind it to the Pod for API access.
NEW QUESTION # 49
What is Serverless computing?
- A. A computing method of providing services for AI and ML operating systems.
- B. A computing method of providing services for quantum computing operating systems.
- C. A computing method of providing backend services on an as-used basis.
- D. A computing method of providing services for cloud computing operating systems.
Answer: C
Explanation:
Serverless computing is a cloud execution model where the provider manages infrastructure concerns and you consume compute as a service, typically billed based on actual usage (requests, execution time, memory), which matches A. In other words, you deploy code (functions) or sometimes containers, configure triggers (HTTP events, queues, schedules), and the platform automatically provisions capacity, scales it up/down, and handles much of availability and fault tolerance behind the scenes.
From a cloud-native architecture standpoint, "serverless" doesn't mean there are no servers; it means developers don't manage servers. The platform abstracts away node provisioning, OS patching, and much of runtime scaling logic. This aligns with the "as-used basis" phrasing: you pay for what you run rather than maintaining always-on capacity.
It's also useful to distinguish serverless from Kubernetes. Kubernetes automates orchestration (scheduling, self-healing, scaling), but operating Kubernetes still involves cluster-level capacity decisions, node pools, upgrades, networking baseline, and policy. With serverless, those responsibilities are pushed further toward the provider/platform. Kubernetes can enable serverless experiences (for example, event-driven autoscaling frameworks), but serverless as a model is about a higher level of abstraction than "orchestrate containers yourself." Options B, C, and D are incorrect because they describe specialized or vague "operating system" services rather than the commonly accepted definition. Serverless is not specifically about AI/ML OSs or quantum OSs; it's a general compute delivery model that can host many kinds of workloads.
Therefore, the correct definition in this question is A: providing backend services on an as-used basis.
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NEW QUESTION # 50
How do you perform a command in a running container of a Pod?
- A. kubectl run <pod> -- <command>
- B. kubectl exec <pod> -- <command>
- C. docker exec <pod> <command>
- D. kubectl attach <pod> -i <command>
Answer: B
Explanation:
In Kubernetes, the standard way to execute a command inside a running container is kubectl exec, which is why A is correct. kubectl exec calls the Kubernetes API (API server), which then coordinates with the kubelet on the target node to run the requested command inside the container using the container runtime's exec mechanism. The -- separator is important: it tells kubectl that everything after -- is the command to run in the container rather than flags for kubectl itself.
This is fundamentally different from docker exec. In Kubernetes, you don't normally target containers through Docker/CRI tools directly because Kubernetes abstracts the runtime behind CRI. Also, "Docker" might not even be installed on nodes in modern clusters (containerd/CRI-O are common). So option B is not the Kubernetes-native approach and often won't work.
kubectl run (option C) is for creating a new Pod (or generating workload resources), not for executing a command in an existing container. kubectl attach (option D) attaches your terminal to a running container's process streams (stdin/stdout/stderr), which is useful for interactive sessions, but it does not execute an arbitrary new command like exec does.
In real usage, you often specify the container when a Pod has multiple containers: kubectl exec -it <pod> -c <container> -- /bin/sh. This is common for debugging, verifying config files mounted from ConfigMaps/Secrets, testing DNS resolution, or checking network connectivity from within the Pod network namespace. Because exec uses the API and kubelet, it respects Kubernetes access control (RBAC) and audit logging-another reason it's the correct operational method.
NEW QUESTION # 51
If a Pod was waiting for container images to download on the scheduled node, what state would it be in?
- A. Failed
- B. Succeeded
- C. Unknown
- D. Pending
Answer: D
Explanation:
If a Pod is waiting for its container images to be pulled to the node, it remains in the Pending phase, so D is correct. Kubernetes Pod "phase" is a high-level summary of where the Pod is in its lifecycle. Pending means the Pod has been accepted by the cluster but one or more of its containers has not started yet. That can occur because the Pod is waiting to be scheduled, waiting on volume attachment/mount, or-very commonly- waiting for the container runtime to pull the image.
When image pulling is the blocker, kubectl describe pod <name> usually shows events like "Pulling image
..." and "Successfully pulled image ..." or failures like ImagePullBackOff/ErrImagePull. Even if the node has been assigned (scheduler has set spec.nodeName), the Pod can still be Pending while kubelet and the runtime perform preparation steps.
Why the other phases don't apply:
* Succeeded is for run-to-completion Pods that have finished successfully (typical for Jobs).
* Failed means the Pod terminated and at least one container terminated in failure (and won't be restarted, depending on restartPolicy).
* Unknown is used when the node can't be contacted and the Pod's state can't be reliably determined (rare in healthy clusters).
A subtle but important Kubernetes detail: status "Waiting" reasons like ImagePullBackOff are container states inside .status.containerStatuses, while the Pod phase can still be Pending. So, "waiting for images to download" maps to Pod Pending, with container waiting reasons providing the deeper diagnosis.
Therefore, the verified correct answer is D: Pending.
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NEW QUESTION # 52
Which of the following sentences is true about container runtimes in Kubernetes?
- A. Container runtimes are deprecated, you must install CRI on each node.
- B. If you let iptables see bridged traffic, you don't need a container runtime.
- C. If you enable IPv4 forwarding, you don't need a container runtime.
- D. You must install a container runtime on each node to run pods on it.
Answer: D
Explanation:
A Kubernetes node must have a container runtime to run Pods, so D is correct. Kubernetes schedules Pods to nodes, but the actual execution of containers is performed by a runtime such as containerd or CRI-O. The kubelet communicates with that runtime via the Container Runtime Interface (CRI) to pull images, create sandboxes, and start/stop containers. Without a runtime, the node cannot launch container processes, so Pods cannot transition into running state.
Options A and B confuse networking kernel settings with runtime requirements. iptables bridged traffic visibility and IPv4 forwarding can be relevant for node networking, but they do not replace the need for a container runtime. Networking and container execution are separate layers: you need networking for connectivity, and you need a runtime for running containers.
Option C is also incorrect and muddled. Container runtimes are not deprecated; rather, Kubernetes removed the built-in Docker shim integration from kubelet in favor of CRI-native runtimes. CRI is an interface, not
"something you install instead of a runtime." In practice you install a CRI-compatible runtime (containerd
/CRI-O), which implements CRI endpoints that kubelet talks to.
Operationally, the runtime choice affects node behavior: image management, logging integration, performance characteristics, and compatibility. Kubernetes installation guides explicitly list installing a container runtime as a prerequisite for worker nodes. If a cluster has nodes without a properly configured runtime, workloads scheduled there will fail to start (often stuck in ContainerCreating/ImagePullBackOff
/Runtime errors).
Therefore, the only fully correct statement is D: each node needs a container runtime to run Pods.
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NEW QUESTION # 53
CI/CD stands for:
- A. Continuous Integration / Continuous Deployment
- B. Continuous Integration / Continuous Development
- C. Cloud Integration / Cloud Development
- D. Continuous Information / Continuous Development
Answer: A
Explanation:
CI/CD is a foundational practice for delivering software rapidly and reliably, and it maps strongly to cloud native delivery workflows commonly used with Kubernetes. CI stands for Continuous Integration:
developers merge code changes frequently into a shared repository, and automated systems build and test those changes to detect issues early. CD is commonly used to mean Continuous Delivery or Continuous Deployment depending on how far automation goes. In many certification contexts and simplified definitions like this question, CD is interpreted as Continuous Deployment, meaning every change that passes the automated pipeline is automatically released to production. That matches option D.
In a Kubernetes context, CI typically produces artifacts such as container images (built from Dockerfiles or similar build definitions), runs unit/integration tests, scans dependencies, and pushes images to a registry. CD then promotes those images into environments by updating Kubernetes manifests (Deployments, Helm charts, Kustomize overlays, etc.). Progressive delivery patterns (rolling updates, canary, blue/green) often use Kubernetes-native controllers and Service routing to reduce risk.
Why the other options are incorrect: "Continuous Development" isn't the standard "D" term; it's ambiguous and not the established acronym expansion. "Cloud Integration/Cloud Development" is unrelated. Continuous Delivery (in the stricter sense) means changes are always in a deployable state and releases may still require a manual approval step, while Continuous Deployment removes that final manual gate. But because the option set explicitly includes "Continuous Deployment," and that is one of the accepted canonical expansions for CD, D is the correct selection here.
Practically, CI/CD complements Kubernetes' declarative model: pipelines update desired state (Git or manifests), and Kubernetes reconciles it. This combination enables frequent releases, repeatability, reduced human error, and faster recovery through automated rollbacks and controlled rollout strategies.
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NEW QUESTION # 54
Which of the following is not a stop on the cloud native trailmap?
- A. Microservices
- B. Containerization
- C. CI/CD
- D. Software distribution
Answer: A
Explanation:
https://github.com/cncf/landscape#trail-map
NEW QUESTION # 55
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