NCP-AIO Installation and Deployment Practice Question
A platform engineer is preparing an Ubuntu 22.04 server that will host GPU-accelerated inference containers managed by containerd (not Docker). The team wants the NVIDIA Container Toolkit to expose GPUs to those containers. After installing the toolkit packages, which action must the engineer take so that containerd actually invokes the NVIDIA runtime for GPU workloads?
⚠ Common exam trap
The trap here is assuming that installing the NVIDIA Container Toolkit packages is sufficient and that containers automatically gain GPU access without reconfiguring the container engine's runtime.
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
✓
Run nvidia-ctk runtime configure --runtime=containerd and restart the containerd service.
Registering the NVIDIA runtime with the container engine is the essential post-install step. The nvidia-ctk runtime configure command writes the runtime entry and default-runtime setting into containerd's config.toml, and containerd must be restarted to apply it. Merely installing packages or setting container environment variables does not change which runtime containerd uses, so GPU devices remain invisible to containers until the engine configuration is updated and reloaded.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Install the nvidia-container-runtime package and symlink it as /usr/bin/runc on the host.
Why it's wrong here
Replacing runc with a symlink to nvidia-container-runtime is an unsupported hack that breaks non-GPU containers and can destabilize the node. The correct integration registers a separate NVIDIA runtime in containerd's configuration rather than overwriting the default low-level runtime binary that all containers rely on.
- ✗
Add the user to the video group and grant read-write access to /dev/nvidiactl.
Why it's wrong here
Linux device permissions matter for direct host access, but containers receive GPU device nodes through the NVIDIA runtime's mount and cgroup logic, not through the invoking user's group membership. Adjusting group membership does not make containerd select the NVIDIA runtime, so containerized GPU access still fails.
- ✗
Set the environment variable NVIDIA_VISIBLE_DEVICES=all in the host shell profile and reboot the node.
Why it's wrong here
NVIDIA_VISIBLE_DEVICES is a container-level environment variable consumed by the NVIDIA runtime when a container is created; it does not configure the container engine itself. Exporting it in the host shell has no effect on containerd's runtime selection, so GPU workloads would still launch under the default runtime with no device access.
- ✓
Run nvidia-ctk runtime configure --runtime=containerd and restart the containerd service.
Why this is correct
The nvidia-ctk runtime configure command edits the containerd configuration (typically /etc/containerd/config.toml) to register the NVIDIA runtime and set it as the default, and containerd must then be restarted to load the change. Without this registration, containerd keeps using its stock runc runtime and GPU devices never appear inside containers, even though the toolkit binaries are installed.
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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.