KCNA Kubernetes Fundamentals Practice Question
Which component runs on each worker node and ensures that containers are running as specified in the Pod spec?
⚠ Common exam trap
A common trap is confusing the kubelet (a node-level agent that runs on each worker and directly manages containers) with control-plane components like the kube-scheduler or kube-controller-manager, which run on the master node and handle cluster-level decisions.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
kubelet
The kubelet is the primary node agent that runs on every worker node in a Kubernetes cluster. It receives PodSpec definitions (via the API server or a file) and ensures that the containers described in those PodSpecs are running and healthy. It does this by interacting with the container runtime (e.g., containerd or CRI-O) to start, stop, and monitor containers, and it reports the node and pod status back to the control plane.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
kubelet
Why this is correct
The kubelet is the node agent that watches the API server for PodSpecs bound to its node and drives the container runtime to start, stop and restart containers so actual state matches the spec. It satisfies the per-worker-node constraint, unlike control-plane components such as the scheduler or controller manager.
- ✗
kube-proxy
Why it's wrong here
kube-proxy runs on each worker node but maintains network rules for Service load balancing and Pod connectivity; it does not start or restart containers. It is tempting because it is a per-node component, yet it operates at the networking layer rather than the container runtime layer.
- ✗
kube-scheduler
Why it's wrong here
The kube-scheduler runs on the control plane and assigns Pods to nodes based on resource requests and constraints; it does not start or monitor containers on a node. It is tempting because it places Pods, but placement is a one-off decision, whereas the stem requires continuous container supervision.
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kube-controller-manager
Why it's wrong here
The kube-controller-manager runs on the control plane and reconciles cluster-level controllers such as Deployments and ReplicaSets; it does not run on worker nodes or supervise individual containers. It is tempting because it enforces desired state, but that is at the API-object level, not the node's container runtime.
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