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Linux Foundation KCNA Certification Exam is an online, proctored exam that consists of 40 multiple-choice questions. KCNA exam is conducted on the EdX platform, which is a leading online learning platform. KCNA exam is designed to test the candidate's understanding of the fundamentals of Kubernetes, containerization, and cloud-native technologies. KCNA exam covers topics such as Kubernetes architecture, deployment, configuration, and troubleshooting.
NEW QUESTION # 201
How does Horizontal Pod autoscaling work in Kubernetes?
Answer: D
Explanation:
Horizontal Pod Autoscaling (HPA) adjusts the number of Pod replicas for a workload controller (most commonly a Deployment) based on observed metrics, increasing replicas when load is high and decreasing when load drops. That matches D, so D is correct.
HPA does not add CPU or memory to existing Pods-that would be vertical scaling (VPA). Instead, HPA changes spec.replicas on the target resource, and the controller then creates or removes Pods accordingly.
HPA commonly scales based on CPU utilization and memory (resource metrics), and it can also scale using custom or external metrics if those are exposed via the appropriate Kubernetes metrics APIs.
Option A is vertical scaling behavior, not HPA. Option B is incorrect because HPA can scale down as well as up (subject to stabilization windows and configuration), so it's not "scale up only." Option C is incorrect because HPA does not scale DaemonSets in the usual model; DaemonSets are designed to run one Pod per node (or per selected nodes) rather than a replica count. HPA targets resources like Deployments, ReplicaSets (via Deployment), and StatefulSets in typical usage, where replica count is a meaningful knob.
Operationally, HPA works as a control loop: it periodically reads metrics (for example, via metrics-server for CPU/memory, or via adapters for custom metrics), compares the current value to the desired target, and calculates a desired replica count within min/max bounds. To avoid flapping, HPA includes stabilization behavior and cooldown logic so it doesn't scale too aggressively in response to short spikes or dips. You can configure minimum and maximum replicas and behavior policies to tune responsiveness.
In cloud-native systems, HPA is a key elasticity mechanism: it enables services to handle variable traffic while controlling cost by scaling down during low demand. Therefore, the verified correct answer is D.
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NEW QUESTION # 202
Observability and monitoring are not the same?
Answer: B
NEW QUESTION # 203
In a Kubernetes cluster, what is the primary difference between a control-plane node and a worker node?
Answer: A
Explanation:
Control-plane nodes host components that maintain the cluster's desired state, make scheduling decisions, and manage Kubernetes resources. Worker nodes run the application workloads inside Pods.
NEW QUESTION # 204
What fields must exist in any Kubernetes object (e.g. YAML) file?
Answer: B
Explanation:
Any Kubernetes object manifest must include apiVersion, kind, and metadata, which makes A correct. This comes directly from how Kubernetes resources are represented and processed by the API server.
apiVersion tells Kubernetes which API group and version should be used to interpret the object (for example v1, apps/v1, batch/v1). This matters because schemas and available fields can change between versions.
kind specifies the type of object you are creating (for example Pod, Service, Deployment, ConfigMap). Kubernetes uses this to route the request to the correct API endpoint and schema.
metadata contains identifying and organizational information such as name, namespace (when namespaced), labels, and annotations. At minimum, most objects require a name; labels and annotations are optional but extremely common for selection and tooling.
A common point of confusion is spec. Many Kubernetes objects include spec because they define desired state (like a Deployment's replica count, Pod template, update strategy). However, the question asks what fields must exist in any Kubernetes object file. Not all objects require a spec in the same way (and some objects include other top-level sections like data for ConfigMaps/Secrets or rules for RBAC objects). The truly universal top-level requirements are the trio in option A.
Options B, C, and D include fields that are not universally required (namespace is not required for cluster-scoped objects, and data only applies to certain kinds like ConfigMaps/Secrets). Therefore, apiVersion + kind + metadata is the correct, general rule and matches Kubernetes object structure.
NEW QUESTION # 205
You have a Kubernetes cluster with a custom network plugin that provides advanced networking capabilities. What needs to be considered when configuring this custom network plugin?
Answer: A,B,C,D,E
Explanation:
All of the options are important considerations when configuring a custom network plugin in Kubernetes- - A Adherence to the CNI specification ensures interoperability with Kubernetes and other CNI plugins- - B: Support for service discovery and load balancing is crucial for seamless integration with Kubernetes services. - C: Integration with the Kubernetes API server allows the plugin to be managed and controlled by Kubernetes- - D Deploying the plugin as a DaemonSet ensures its availability and consistent behavior across all nodes. - E Thorough testing is essential to guarantee compatibility with your applications and the desired performance characteristics.
NEW QUESTION # 206
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