NCP-AIO Administration Practice Question
Which THREE components are required for a container to successfully leverage NVIDIA GPUs on a Kubernetes cluster?
⚠ Common exam trap
Candidates often forget the role of the NVIDIA Container Toolkit, incorrectly assuming that the driver and device plugin alone are sufficient to bridge the container runtime to the GPU.
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 Container Toolkit
Successful GPU integration in Kubernetes relies on the interaction between the container engine, the NVIDIA driver, and the device plugin. The driver provides the kernel-level interface, the container runtime handles the low-level injection, and the device plugin advertises the hardware to the scheduler. These three parts must be correctly configured and compatible to ensure that containers can schedule and utilize GPUs effectively without manual configuration or persistent connectivity issues.
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 Container Toolkit
Why this is correct
The NVIDIA Container Toolkit is essential for enabling the container runtime to interact with the host's NVIDIA drivers. It performs the necessary steps to inject the GPU devices into the container namespace and ensures that the required libraries are mapped correctly so the application can communicate with the GPU hardware.
- ✓
Kubernetes Device Plugin for NVIDIA
Why this is correct
The device plugin is responsible for advertising the available GPU resources to the Kubernetes scheduler. Without this plugin, the scheduler is unaware of the GPU presence and will be unable to place pods onto nodes that have physical GPU resources available, effectively rendering the cluster unable to schedule GPU workloads.
- ✗
NVIDIA Data Center GPU Manager (DCGM)
Why it's wrong here
While DCGM is a valuable tool for monitoring and managing GPU health, it is not strictly required for a container to simply run a workload on a GPU. A container can function perfectly well without DCGM installed, provided the driver and device plugin are correctly configured and operational.
- ✓
NVIDIA Driver installed on the host node
Why this is correct
The host node must have the appropriate NVIDIA driver installed and loaded to provide the necessary kernel-level interface for the hardware. Without the host-side driver, there is no way for the containerized application to communicate with the GPU hardware regardless of how well the container runtime is configured.
- ✗
Pre-installed CUDA Toolkit inside the container
Why it's wrong here
The CUDA toolkit is an application-level dependency. While it is needed for building applications, it is not required for the infrastructure to support GPU workloads. In many production environments, the runtime libraries are dynamically mounted or provided through a base image, making a full pre-installed toolkit unnecessary for general execution.
About these practice questions
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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.