The standard way to deploy workloads to Kubernetes using only built-in tooling is to create Kubernetes manifests (YAML/JSON definitions of API objects) and apply them with kubectl, so C is correct. Kubernetes is a declarative system: you describe the desired state of resources (e.g., a Deployment, Service, ConfigMap, Ingress) in a manifest file, then submit that desired state to the API server. Controllers reconcile the actual cluster state to match what you declared.
A manifest typically includes mandatory fields like apiVersion, kind, and metadata, and then a spec describing desired behavior. For example, a Deployment manifest declares replicas and the Pod template (containers, images, ports, probes, resources). Applying the manifest with kubectl apply -f creates or updates the resources. kubectl apply is also designed to work well with iterative changes: you update the file, re-apply, and Kubernetes performs a controlled rollout based on controller logic.
Option B (Helm) is indeed a popular deployment tool, but Helm is explicitly an “additional tool†beyond kubectl and the Kubernetes API. The question asks “without additional tools,†so Helm is excluded by definition. Option A (running Bash scripts on worker nodes) bypasses Kubernetes’ desired-state control and is not how Kubernetes workload deployment is intended; it also breaks portability and operational safety. Option D is not a standard Kubernetes deployment mechanism; kubectl does not “run Python scripts†to deploy workloads (though scripts can automate kubectl, that’s still not the primary mechanism).
From a cloud native delivery standpoint, manifests support GitOps, reviewable changes, and repeatable deployments across environments. The Kubernetes-native approach is: declare resources in manifests and apply them to the cluster. Therefore, C is the verified correct answer.
