NCP-AIO Workload Management Practice Question
A platform engineer manages an NVIDIA-accelerated Kubernetes cluster running the NVIDIA GPU Operator on nodes with A100 GPUs. Several data-science teams submit training jobs, and the engineer must ensure each team's pods receive a full physical GPU exclusively, with no two pods sharing the same device. Which scheduling configuration should the engineer apply to the pod specification to guarantee exclusive whole-GPU allocation?
⚠ Common exam trap
The trap here is assuming that a nodeSelector or hostPath device mount can enforce exclusivity, when only the nvidia.com/gpu extended resource request actually reserves a whole physical GPU through the device plugin.
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
✓
Set resources.limits to nvidia.com/gpu: 1 and resources.requests to nvidia.com/gpu: 1, letting the NVIDIA device plugin allocate an exclusive GPU.
Declaring both a request and a limit for nvidia.com/gpu equal to 1 is the canonical way to obtain an exclusive whole GPU in Kubernetes. The NVIDIA device plugin advertises GPUs as integer extended resources, so the scheduler reserves one entire device for the pod. Because extended resources cannot be fractional, no co-scheduling or sharing occurs, which precisely meets the exclusive allocation requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable MIG by setting the nvidia.com/mig.config label on the node to all-balanced and request nvidia.com/mig-1g.5gb in the pod.
Why it's wrong here
MIG partitions a physical GPU into smaller slices, so requesting a mig-1g.5gb profile allocates only a fraction of an A100, not a whole physical GPU. While MIG provides strong isolation between slices, it contradicts the requirement for full-device exclusivity and would reduce the compute and memory available to each training job.
- ✗
Configure the pod to mount the hostPath /dev/nvidia0 and set privileged: true so the container can access the first GPU directly.
Why it's wrong here
Mounting /dev/nvidia0 and running privileged bypasses the device plugin's accounting entirely. Kubernetes would not track GPU usage, so multiple pods could mount the same device and contend for it, and the scheduler could overcommit nodes. This approach also removes the isolation and admission control that the NVIDIA device plugin provides, making it unsuitable for guaranteed exclusive allocation.
- ✗
Set a nodeSelector for nvidia.com/gpu.product=A100 and rely on the scheduler to place one pod per node automatically.
Why it's wrong here
A nodeSelector only filters which nodes are eligible based on the GPU product label; it does not allocate or count GPU devices. Multiple pods with the same nodeSelector can still land on the same node and consume separate GPUs, or fail to be admitted once GPUs are exhausted, but it never guarantees exclusive per-pod whole-GPU allocation by itself.
- ✓
Set resources.limits to nvidia.com/gpu: 1 and resources.requests to nvidia.com/gpu: 1, letting the NVIDIA device plugin allocate an exclusive GPU.
Why this is correct
Requesting and limiting nvidia.com/gpu to 1 makes the NVIDIA device plugin treat the GPU as an indivisible integer device, so the kubelet advertises whole GPUs and the scheduler binds exactly one physical A100 exclusively to the pod. Because the extended resource is integer-only, no sharing occurs, which satisfies the exclusivity requirement without extra configuration.
About these practice questions
This NCP-AIO question is part of Courseiva's 309-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.