NCP-AIO Administration Practice Question
An administrator manages an NVIDIA AI Enterprise cluster running multiple Kubernetes nodes, each with several A100 GPUs. After upgrading the NVIDIA GPU Operator to a newer version, the administrator notices that pods requesting GPUs remain in a Pending state, and the node's allocatable GPU count is reported as zero. Which command should the administrator run first to diagnose the issue?
⚠ Common exam trap
The trap here is assuming that checking the GPU Operator logs or running nvidia-smi will directly explain why Kubernetes reports zero allocatable GPUs, when the node's resource status is the authoritative source.
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 describe node <node-name>
The node's allocatable GPU count is reported as zero, indicating that the kubelet is not receiving GPU resource advertisements from the NVIDIA device plugin. Describing the node reveals capacity, allocatable resources, and relevant events, such as device plugin registration failures. This directly identifies whether the GPU Operator's device plugin is functioning, making it the correct first step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
kubectl describe node <node-name>
Why this is correct
This command shows detailed node status, including conditions, capacity, and allocatable resources. If the GPU Operator's device plugin is not functioning, the node will report zero allocatable nvidia.com/gpu resources, and events may indicate plugin registration failures. It directly reveals whether the node recognizes the GPUs as schedulable resources, making it the essential first diagnostic step.
- ✗
kubectl get pods --all-namespaces -o wide
Why it's wrong here
Listing all pods shows their status and node placement, which can confirm that GPU-requesting pods are Pending, but it does not explain why. It lacks details about node resource availability or device plugin health. This command is too broad and does not provide the specific diagnostic information needed to identify the root cause.
- ✗
nvidia-smi -q
Why it's wrong here
Running nvidia-smi on the host verifies that the driver and GPUs are physically present and healthy, but it does not reflect Kubernetes resource allocation. The issue could be that the device plugin is not exposing GPUs to the kubelet, even if nvidia-smi works. Thus, it does not directly diagnose the Kubernetes-level scheduling problem.
- ✗
kubectl logs -n gpu-operator <gpu-operator-pod>
Why it's wrong here
While checking the GPU Operator pod logs can provide insight into operator-level issues, it does not directly show the node's allocatable resources or device plugin status. The operator may be running fine while the device plugin DaemonSet fails. Therefore, this is not the most immediate command to confirm why the node reports zero GPUs.
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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.