NCP-AIO Installation and Deployment Practice Question
An AI infrastructure team is deploying NVIDIA AI Enterprise on a Kubernetes cluster using the NVIDIA GPU Operator. They need to ensure that the GPU Operator can successfully manage GPUs and that workloads can consume GPU resources. Which two components does the GPU Operator deploy to enable GPU scheduling and container GPU access? (Choose two.)
⚠ Common exam trap
The trap here is assuming that the GPU driver alone is sufficient for GPU scheduling and container access, overlooking the need for the device plugin and container toolkit.
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
The NVIDIA GPU Operator deploys the NVIDIA Device Plugin to enable Kubernetes to schedule pods with GPU resources, and the NVIDIA Container Toolkit to allow containers to access GPUs. The device plugin registers GPUs with the Kubernetes API, while the container toolkit configures the container runtime. Together, they provide the necessary integration for GPU-accelerated workloads. Other components like the driver and MIG manager are important but not the specific enablers for scheduling and container access.
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 Device Plugin
Why this is correct
The NVIDIA Device Plugin is deployed by the GPU Operator to advertise GPU resources to the Kubernetes API server. It allows the scheduler to allocate GPUs to pods that request them. Without it, Kubernetes would not know about the GPUs, and pods would not be scheduled with GPU resources. This component is essential for GPU scheduling.
- ✓
NVIDIA Container Toolkit
Why this is correct
The NVIDIA Container Toolkit is deployed by the GPU Operator to enable container runtimes to access GPUs. It configures the container runtime to inject GPU devices and driver libraries into containers. This allows pods to actually use the GPUs allocated by the scheduler. It is required for container GPU access.
- ✗
NVIDIA Persistence Daemon
Why it's wrong here
The NVIDIA Persistence Daemon is not deployed by the GPU Operator. It is a host-level service that maintains the NVIDIA driver state and is typically used in non-Kubernetes environments. It does not enable GPU scheduling or container access in Kubernetes. Therefore, it is not a component deployed by the GPU Operator for this purpose.
- ✗
NVIDIA MIG Manager
Why it's wrong here
The NVIDIA MIG Manager is an optional component of the GPU Operator used to configure Multi-Instance GPU (MIG) on supported GPUs. It is not required for basic GPU scheduling and container access. While it can be deployed, it is not one of the two essential components that enable GPU scheduling and container GPU access in a standard deployment.
- ✗
NVIDIA GPU Driver
Why it's wrong here
The NVIDIA GPU Driver is installed by the GPU Operator, but it is not a component that enables scheduling or container access directly. It provides the low-level driver, but the device plugin and container toolkit are needed to integrate with Kubernetes and containers. The driver is a prerequisite, but the question asks for components that enable scheduling and container access.
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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.