NCP-AIO Workload Management Practice Question
A platform team runs an NVIDIA GPU Operator-managed Kubernetes cluster shared by two research groups. Group A's pods request `nvidia.com/gpu: 1` and are scheduled correctly, but Group B's pods that omit any GPU resource request are also landing on GPU nodes and consuming host memory and CPU, degrading Group A's jobs. The team wants Group B's non-GPU pods to stop consuming capacity on the GPU node pool without changing Group B's manifests. Which action should the administrator take?
⚠ Common exam trap
The trap here is assuming that a ResourceQuota or device-plugin setting controls node placement, when only taints, tolerations, and node affinity influence the scheduler's decisions.
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
✓
Apply a taint such as `nvidia.com/gpu=present:NoSchedule` to the GPU nodes and add a matching toleration to Group A's pod templates.
Dedicating a node pool to GPU consumers is done at the scheduler layer: a taint on the GPU nodes repels every pod that does not carry a matching toleration. Because Group B's manifests cannot be changed, repelling rather than attracting is the only workable direction, and adding tolerations to Group A's templates is a one-time change under the platform team's control. Quotas, driver flags, and plugin options all operate after or outside scheduling and cannot keep non-GPU pods off those nodes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set `NVIDIA_DRIVER_CAPABILITIES=compute,utility` on the GPU Operator DaemonSet so non-GPU pods cannot attach to the device.
Why it's wrong here
The driver capability environment variable controls which driver libraries are injected into containers that already have GPU access; it has no bearing on the scheduler's decision about which node a pod lands on. Group B's pods would still be placed on GPU nodes and still consume host CPU and memory, so the degradation described would continue unchanged.
- ✗
Create a `ResourceQuota` in each namespace that caps `requests.nvidia.com/gpu` at zero for Group B.
Why it's wrong here
A ResourceQuota limiting GPU requests only rejects pods that actually request GPUs; Group B's pods request none, so the quota never triggers and they continue to schedule onto GPU nodes and consume CPU and memory. Quotas constrain quantity of requested resources, not node placement, so this fails to protect the GPU node pool in this scenario.
- ✗
Enable the `nvidia` device plugin's `--fail-on-init-error=false` flag so unresolvable GPU requests fall back to CPU scheduling.
Why it's wrong here
This flag only affects how the device plugin reports initialization failures; it does not influence pod placement or reserve nodes for GPU consumers. Group B's pods make no GPU request at all, so there is nothing to fall back from, and the pods would keep occupying the GPU node pool's CPU and memory exactly as before.
- ✓
Apply a taint such as `nvidia.com/gpu=present:NoSchedule` to the GPU nodes and add a matching toleration to Group A's pod templates.
Why this is correct
Tainting GPU nodes with a dedicated key and NoSchedule effect prevents any pod lacking a matching toleration from being placed there, so Group B's unmodified workloads are repelled while Group A's templates are explicitly admitted. This is the standard scheduler-level control for dedicating a node pool, and it requires no edits to Group B's manifests, matching the stated constraint exactly.
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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.