NCP-AIO Installation and Deployment Practice Question
An AI operations engineer is deploying a multi-node Kubernetes cluster with NVIDIA A100 GPUs for distributed training. The engineer wants to ensure that GPUs are correctly discovered and that workloads can request GPU resources. After installing the NVIDIA GPU Operator, the engineer notices that the GPU nodes are not advertising any 'nvidia.com/gpu' resources. Which component should the engineer verify first to resolve this issue?
⚠ Common exam trap
The trap here is focusing on the driver or toolkit first; however, the device plugin is the component that directly advertises GPU resources to Kubernetes.
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
✓
NVIDIA Device Plugin
When GPU resources are not advertised, the NVIDIA Device Plugin is the primary component to investigate. It runs as a DaemonSet on GPU nodes and registers GPUs with the kubelet. Issues such as pod failures, missing driver libraries, or misconfigured kubelet settings can prevent resource advertisement. Checking its logs and status will reveal the root cause.
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 DCGM Exporter
Why it's wrong here
DCGM Exporter is responsible for exposing GPU metrics, not for advertising GPU resources to the Kubernetes scheduler. If it fails, monitoring data would be missing, but GPU resource advertisement would still occur if the device plugin is working. Therefore, checking DCGM Exporter first is not the correct troubleshooting step for missing GPU resources.
- ✗
NVIDIA GPU Operator's driver container
Why it's wrong here
The driver container installs the NVIDIA driver. If the driver failed to install, the device plugin would also fail, but the driver container is not directly responsible for advertising resources. The engineer should first check the device plugin because it is the component that registers GPU resources. The driver is a prerequisite, but the immediate symptom is lack of resource advertisement.
- ✗
NVIDIA Container Toolkit
Why it's wrong here
The Container Toolkit enables containers to access GPUs but does not advertise resources. Even if the toolkit is misconfigured, the device plugin might still advertise GPUs, though containers would fail to use them. The symptom of no GPU resources points to the device plugin, not the toolkit. Verifying the toolkit would not resolve the missing resource advertisement.
- ✓
NVIDIA Device Plugin
Why this is correct
The NVIDIA Device Plugin is the component that discovers GPUs and advertises them as schedulable resources (e.g., nvidia.com/gpu) to the Kubernetes API server. If GPUs are not appearing as resources, the device plugin is the first component to check. It could be failing to start, unable to communicate with the kubelet, or missing driver dependencies.
Visual reference
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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.