VMware 3V0-24.25 Practice Test Questions Answers

In Kubernetes terms,non-persistentpod data (for example, transient logs and scratch space) is handled byephemeral storage, meaning the data exists only for the lifetime of the pod/workload and is not meant to survive beyond it. In the VCF Workload Management documentation, this concept is described directly: a pod requiresephemeral storageto store transient Kubernetes objects such as “logs” and “emptyDir volumes,” and this ephemeral (transient) storage “lasts as long as the pod continues to exist,” disappearing when the pod reaches end of life.

While VKS clusters can also consumepersistent storagethrough storage classes and CSI integration for stateful needs, that is specifically for data that must be retained (persistent volumes/claims). The question asks specifically aboutnon-persistentpod data, which aligns with the documented ephemeral/transient storage behavior for pod runtime needs. Therefore, the correct choice isEphemeral storage.

In Kubernetes, the component that actuallycreates and runs workloads on a nodeis thekubelet. The kubelet is the node agent that ensures the containers described by PodSpecs are running on that node. VCF 9.0 maps this concept directly into vSphere Supervisor by describingSphereletas “a kubelet that is ported natively to ESXi and allows the ESXi host to become part of the Kubernetes cluster,” showing that kubelet functionality is responsible for running workloads on worker nodes (ESXi hosts in the Supervisor case).

The other options have different roles:etcdis the control plane data store,API Serveris the front-end for Kubernetes API operations, and theSchedulerdecides placement (which node should run a pod). VCF 9.0 even calls out that “the Kubernetes scheduler… cannot place pods intelligently” without visibility into vCenter inventory—reinforcing that scheduling is about placement decisions, not the act of creating/running the workload on the node.

So, while the scheduler selects where a pod should run, thekubeletis the component responsible for actually instantiating and maintaining the workload on the target node.

VCF 9.0 explains pod fundamentals by describing how Workload Management introducesvSphere Pods, stating a vSphere Pod is “equivalent of a Kubernetes pod” and that it “runs one or more Linux containers.” This directly eliminates optionB, because a pod can includeone or morecontainers (not only one).

The vSphere 9.0 documentation further defines a KubernetesPodas “a group of one or more containerized applications that share such resources as storage and network,” and notes the containers inside a pod are “started, stopped, and replicated as a group.” That definition reflects Kubernetes’ scheduling and lifecycle model: Kubernetes treats the pod as the primary unit it places and manages together, which is why a pod is regarded as thesmallest deployable unitfor running containerized workloads in Kubernetes. OptionsCandDare incorrect because pods are Kubernetes objects (not “managed by Docker” as a smallest entity), and Kubernetes abstracts the underlying runtime/host so pods are not defined as being “deployed directly on the virtual machine” as a characteristic.

VKS cluster lifecycle is managed using adeclarative API: you usekubectl with a YAML fileto specify the desired state of the cluster (for example: “how many nodes,” Kubernetes version, sizing, and storage). After the cluster is created, youupdate the YAMLto update the cluster. This is why the correct operational approach is to modify the cluster manifest (cluster.yaml) rather than deleting and redeploying.

Additionally, VKS uses multiple controller layers, whereCluster APIand theVirtual Machine Serviceare responsible for provisioning and managing the lifecycle of the control plane and worker node VMs that make up the VKS cluster. In other words, when you change the declared state for control plane sizing/replica count in the cluster YAML, the platform reconciles to that new state by adjusting the underlying control plane VMs through the supported controllers, instead of requiring disruptive “tear down and rebuild” operations.

So, editing the cluster.yaml to adjust the control plane replica count is the method that matches the documented VKS declarative operations model and controller-driven reconciliation.

Answer (Correct Order):

Navigate in VCF Operations to Fleet Management → Tasks, select the instance and review Tasks

Check for errors or failed ClusterConfig objects

SSH into the Supervisor Cluster to review logs / attempt manual cleanup if required

Restart the WCP Service

To determinewhya Supervisor upgrade failed, you start where the platform records the most actionable failure context: themanagement-plane task history. The Fleet/Task view surfaces the upgrade workflow steps, timestamps, and the specific operation that failed, which gives you the fastest pointer to the failing phase (precheck, image staging, reconciliation, add-on updates, etc.). Next, you validate the Kubernetes-side reconciliation state by checking forfailed ClusterConfigobjects, because Supervisor enablement/upgrade actions commonly drive configuration via controller reconciliation; a failed ClusterConfig often contains the clearest “reason” and “message” fields that explain what blocked progress (validation errors, dependency issues, unreachable endpoints, incompatible components).

If the management-plane/task view and ClusterConfig status still don’t isolate the root cause, the next escalation is toSSH into the Supervisorto inspect component logs/events and identify low-level failures that may not bubble up cleanly (for example, admission failures, controller crashes, or connectivity/auth issues). Finally,restarting WCPis a remediation step that can clear stuck states, but it’s placed last because it can alter evidence and should not be your first action when you’re still trying to identify the original failure cause.