NCP-AIO Workload Management Practice Question
An AI operations engineer manages a Kubernetes cluster where the NVIDIA GPU Operator's device plugin exposes GPUs as schedulable resources. A data science team submits a batch inference job that requests one GPU but does not specify a node selector or tolerations. The job stays in Pending while other GPU nodes remain idle because they carry a taint the GPU Operator applied to reserve them for a specific workload class. Which approach is the most appropriate for the engineer to make the job schedulable without disrupting the reserved nodes?
⚠ Common exam trap
The trap here is assuming a Pending GPU pod is caused by missing GPU resources or quota, when the actual cause is an un-tolerated node taint.
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
✓
Add an appropriate toleration and node selector to the job so it can target the reserved GPU nodes.
A node taint repels pods unless they carry a matching toleration. The reserved GPU nodes are intentionally tainted, so a job that neither tolerates the taint nor selects an untainted node cannot schedule. Adding the correct toleration plus a node selector places the job on the reserved nodes while preserving the reservation for other classes of work.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add an appropriate toleration and node selector to the job so it can target the reserved GPU nodes.
Why this is correct
A taint on a node only repels pods that do not tolerate it. Adding the matching toleration and a node selector that identifies the reserved GPU nodes lets this job schedule there while every other untolerated workload stays off those nodes. The reservation remains intact and the job becomes runnable, which directly resolves the Pending state.
- ✗
Delete and recreate the device plugin daemonset so GPUs are re-advertised to the scheduler.
Why it's wrong here
The device plugin is already advertising GPUs correctly; that is why other jobs run. The Pending state stems from a taint that the pod does not tolerate, not from missing resource advertisement. Restarting the device plugin would briefly interrupt GPU allocation cluster-wide and would not change the scheduling outcome for this job.
- ✗
Increase the cluster's GPU resource quota in the namespace so the scheduler can allocate a card.
Why it's wrong here
A ResourceQuota limits how much a namespace may consume; it does not influence node taints or scheduling decisions. The job is Pending because no node will admit it, not because the namespace is out of quota. Raising quota would leave the taint in place and the pod would still find no eligible node, so the problem persists.
- ✗
Remove the node taint from all GPU nodes so the job can be scheduled anywhere.
Why it's wrong here
Removing the taint from every GPU node would allow the pending job to schedule, but it also destroys the reservation that protects those nodes for the specific workload class. This sacrifices the intended isolation and could let unrelated jobs consume GPU capacity that was deliberately set aside, which is far more disruptive than the original scheduling problem.
About these practice questions
One of 309 original NCP-AIO practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.