NCP-AIO Installation and Deployment Practice Question
An AI operations team is deploying the NVIDIA GPU Operator on a Kubernetes cluster that uses containerd as the container runtime. The cluster nodes have NVIDIA GPUs, and the team wants to ensure that GPU workloads can request GPU resources. After installing the operator, they notice that pods requesting 'nvidia.com/gpu' remain in Pending state. Which component of the GPU Operator is most likely misconfigured or missing?
⚠ Common exam trap
Watch out — candidates often confuse the role of the NVIDIA Container Toolkit with that of the device plugin; the toolkit enables GPU access at runtime, but the device plugin is what makes GPUs visible to the scheduler.
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
✓
The NVIDIA device plugin, which advertises GPU resources to the Kubernetes API server.
The NVIDIA device plugin is a DaemonSet that runs on each node and registers GPUs as extended resources. When it is missing or misconfigured, the Kubernetes API server has no knowledge of available GPUs, so any pod requesting 'nvidia.com/gpu' cannot be scheduled. Ensuring the device plugin is healthy and running is essential for GPU scheduling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The NVIDIA device plugin, which advertises GPU resources to the Kubernetes API server.
Why this is correct
The NVIDIA device plugin is responsible for discovering GPUs on each node and advertising them as schedulable resources like 'nvidia.com/gpu'. If it is not running or misconfigured, the Kubernetes scheduler will not see any GPU resources, causing pods that request them to remain Pending. This is the most direct cause for the described symptom.
- ✗
The Kubernetes scheduler configuration, which may not be aware of GPU resources.
Why it's wrong here
The Kubernetes scheduler automatically handles extended resources like 'nvidia.com/gpu' once they are advertised by a device plugin. There is no special scheduler configuration required. The issue is not the scheduler itself but the absence of the device plugin that registers the resource. Therefore, this is not the most likely cause.
- ✗
The NVIDIA Container Toolkit, which enables containers to access GPUs.
Why it's wrong here
The NVIDIA Container Toolkit is necessary for containers to use GPUs, but it does not advertise GPU resources to Kubernetes. Without the device plugin, the scheduler would not know GPUs exist, so pods would still be Pending even if the toolkit is correctly installed. The toolkit affects runtime behavior, not scheduling.
- ✗
The GPU Operator's driver container, which loads the NVIDIA kernel modules.
Why it's wrong here
The driver container ensures the GPU drivers are loaded on the host. If it failed, the device plugin would not detect GPUs, but the immediate symptom of Pending pods is due to missing resource advertisement. The driver container is a prerequisite, but the device plugin is the component that directly translates GPU availability into Kubernetes resources.
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.