Kubernetes and Cloud Native KCNA Linux Foundation Study Notes

A Kubernetes Service without selectors requires you to manage its backend endpoints manually, so B is correct. Normally, a Service uses a selector to match a set of Pods (by labels). Kubernetes then automatically maintains the backend list (historically Endpoints, now commonly EndpointSlice) by tracking which Pods match the selector and are Ready. This automation is one of the key reasons Services provide stable connectivity to dynamic Pods.

When you create a Service without a selector, Kubernetes has no way to know which Pods (or external IPs) should receive traffic. In that pattern, you explicitly create an Endpoints object (or EndpointSlices, depending on your approach and controller support) that maps the Service name to one or more IP:port tuples. This is commonly used to represent external services (e.g., a database running outside the cluster) while still providing a stable Kubernetes Service DNS name for in-cluster clients. Another use case is advanced migration scenarios where endpoints are controlled by custom controllers rather than label selection.

Why the other options are wrong: Service types like ClusterIP, NodePort, and LoadBalancer describe how a Service is exposed, but they do not inherently require manual endpoint management. A ClusterIP Service with selectors (D) is the standard case where endpoints are automatically created and updated. NodePort and LoadBalancer Services also typically use selectors and therefore inherit automatic endpoint management; the difference is in how traffic enters the cluster, not how backends are discovered.

Operationally, when using Services without selectors, you must ensure endpoint IPs remain correct, health is accounted for (often via external tooling), and you update endpoints when backends change. The key concept is: no selector → Kubernetes can’t auto-populate endpoints → you must provide them.

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The liveness probe in Kubernetes is designed to detect whether a container is still running correctly or has entered a failed or unresponsive state. Its primary purpose is to determine whether a container should be restarted. When a liveness probe fails repeatedly, Kubernetes assumes the container is unhealthy and automatically restarts it to restore normal operation.

Option D correctly describes this behavior. Liveness probes are used to identify situations where an application is running but no longer functioning as expected—for example, a deadlock, infinite loop, or hung process that cannot recover on its own. In such cases, restarting the container is often the most effective remediation, and Kubernetes handles this automatically through the liveness probe mechanism.

Option A is incorrect because readiness probes—not liveness probes—determine whether a container is ready to receive traffic. A container can be alive but not ready, such as during startup or temporary maintenance. Option B is incorrect because startup success is handled by startup probes, which are specifically designed to manage slow-starting applications and delay liveness and readiness checks until initialization is complete. Option C is incorrect because exceeding resource limits is managed by the container runtime and kubelet (for example, OOMKills), not by probes.

Liveness probes can be implemented using HTTP requests, TCP socket checks, or command execution inside the container. If the probe fails beyond a configured threshold, Kubernetes restarts the container according to the Pod’s restart policy. This self-healing behavior is a core feature of Kubernetes and contributes significantly to application reliability.

Kubernetes documentation emphasizes using liveness probes carefully, as misconfiguration can cause unnecessary restarts. However, when used correctly, they provide a powerful way to automatically recover from application-level failures that Kubernetes cannot otherwise detect.

In summary, the liveness probe’s role is to detect when a container is unresponsive and needs to be restarted, making option D the correct and fully verified answer.

A is correct: Kubernetes’ API can be extended by adding CustomResourceDefinitions (CRDs) and/or by implementing the API Aggregation Layer. These are the two canonical extension mechanisms.

CRDs let you define new resource types (new kinds) that the Kubernetes API server stores in etcd and serves like native objects. You typically pair a CRD with a controller/operator that watches those custom objects and reconciles real resources accordingly. This pattern is foundational to the Kubernetes ecosystem (many popular add-ons install CRDs).

The aggregation layer allows you to add entire API services (aggregated API servers) that serve additional endpoints under the Kubernetes API. This is used when you want custom API behavior, custom storage, or specialized semantics beyond what CRDs provide (or when implementing APIs like metrics APIs historically).

Why the other answers are wrong:

B is not how API extension works. You don’t “extend the API” by inventing new versions like v4beta3; versions are defined and implemented by API servers/controllers, not by users arbitrarily.

C is fictional; there is no standard kubectl extend api command.

D is also incorrect; kubelet parameters configure node agent behavior, not API server types and discovery.

So, the verified ways to extend Kubernetes’ API surface are CRDs and API aggregation, which is option A.

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Kubernetes groups and organizes objects primarily using labels, so B is correct. Labels are key-value pairs attached to objects (Pods, Deployments, Services, Nodes, etc.) and are intended to be used for identifying, selecting, and grouping resources in a flexible, user-defined way.

Labels enable many core Kubernetes behaviors. For example, a Service selects the Pods that should receive traffic by matching a label selector against Pod labels. A Deployment’s ReplicaSet similarly uses label selectors to determine which Pods belong to the replica set. Operators and platform tooling also rely on labels to group resources by application, environment, team, or cost center. This is why labeling is considered foundational Kubernetes hygiene: consistent labels make automation, troubleshooting, and governance easier.

A “label selector” (option C) is how you query/group objects based on labels, but the underlying primary mechanism is still the labels themselves. Without labels applied to objects, selectors have nothing to match. Custom Resources (option A) extend the API with new kinds, but they are not the primary grouping mechanism across the cluster. “Pod” (option D) is a workload unit, not a grouping mechanism.

Practically, Kubernetes recommends common label keys like app.kubernetes.io/name, app.kubernetes.io/instance, and app.kubernetes.io/part-of to standardize grouping. Those conventions improve interoperability with dashboards, GitOps tooling, and policy engines.

So, when the question asks for the primary mechanism used to identify grouped objects in Kubernetes, the most accurate answer is Labels (B)—they are the universal metadata primitive used to group and select resources.

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