NCP-AIO Administration Practice Question
An administrator is setting up an NVIDIA AI Enterprise cluster and wants to verify that the NVIDIA GPU Operator has successfully deployed all required components on a worker node. Which command should the administrator use to list the GPU Operator pods running on that node?
⚠ Common exam trap
It's easy for candidates to confuse Helm release status with actual pod health, when verifying operator deployment on a node requires inspecting pods filtered by node name.
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
✓
kubectl get pods -n gpu-operator --field-selector spec.nodeName=<node-name>
The NVIDIA GPU Operator runs its components as pods in the gpu-operator namespace. To confirm deployment on a specific worker node, filtering pods by spec.nodeName in that namespace lists exactly the operator pods scheduled there. This provides direct evidence that the driver, container toolkit, device plugin, and related daemonsets are running on the node.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
nvidia-smi -q -d COMPUTE
Why it's wrong here
nvidia-smi queries the local GPU driver and reports hardware and compute details, but it does not list Kubernetes pods or show GPU Operator components. It can confirm that a driver is loaded on a node, yet it provides no visibility into whether the operator's daemonsets and device plugin pods are healthy. For verifying operator deployment, this command answers a different question and is therefore not appropriate here.
- ✗
kubectl describe node <node-name> | grep nvidia
Why it's wrong here
Describing a node shows allocatable resources and taints, and grepping for nvidia may reveal advertised GPU capacity, but it does not enumerate the GPU Operator's pods. It cannot confirm that the driver container, container toolkit, or DCGM exporter pods are running. While useful for checking resource advertisement, this approach does not directly verify that all operator components were deployed successfully on the node.
- ✓
kubectl get pods -n gpu-operator --field-selector spec.nodeName=<node-name>
Why this is correct
The GPU Operator deploys its components into the gpu-operator namespace by default, and the field selector spec.nodeName filters pods to a specific node. This command directly lists the operator pods on the target node, allowing the administrator to confirm that components such as the driver daemonset, container toolkit, and device plugin are running. It is the precise and efficient way to verify operator deployment on a worker node.
- ✗
helm list -n gpu-operator
Why it's wrong here
helm list shows installed Helm releases and their status, which confirms that the GPU Operator chart was deployed, but it does not reveal the runtime state of individual pods on a node. A release can be marked deployed while some daemonset pods are crashlooping or pending. To verify actual component health per node, the administrator needs pod-level information rather than release-level metadata.
About these practice questions
Courseiva writes every NCP-AIO question from scratch — 309 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-AIO practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-AIO exam.