NCP-AIO Administration Practice Question
An administrator manages an NVIDIA AI Enterprise cluster using NVIDIA Run:ai. A data science team complains that their submitted training job has been stuck in a Pending state for over an hour, even though the Run:ai scheduler shows free GPUs in the cluster. The administrator verifies that the job requests 2 GPUs and the node pool has 4 idle GPUs. Which Run:ai administrative configuration is the most likely cause of the job remaining Pending?
⚠ Common exam trap
The trap here is assuming that free GPUs in the cluster automatically mean a job can be scheduled, ignoring project-level quota enforcement in Run:ai.
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
✓
The project's GPU quota is exhausted by other running workloads, so the scheduler cannot allocate the requested GPUs.
Run:ai uses projects to enforce GPU quotas. When a project's quota is fully consumed, new jobs remain Pending even if the cluster has idle GPUs. The administrator should check the project's quota usage and either increase the quota or free resources. Node affinity, missing GPU Operator, or missing Container Toolkit would produce different symptoms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The job's container image does not include the NVIDIA Container Toolkit, so the scheduler cannot start the job.
Why it's wrong here
The NVIDIA Container Toolkit is typically injected by the GPU Operator at the node level, not baked into the image. Missing toolkit would cause a runtime failure after scheduling, not a Pending state. The job would be scheduled and then fail, so this is not the cause.
- ✓
The project's GPU quota is exhausted by other running workloads, so the scheduler cannot allocate the requested GPUs.
Why this is correct
Run:ai projects enforce a GPU quota per project. Even if the cluster has idle GPUs, a job in a project that has already consumed its quota will remain Pending until quota is freed. This matches the scenario where the cluster shows free GPUs but the job cannot start because the project-level quota is the limiting factor.
- ✗
The NVIDIA GPU Operator is not installed on the cluster, so the scheduler cannot see GPU resources.
Why it's wrong here
If the GPU Operator were missing, the cluster would not report any GPUs at all, and the administrator would not see idle GPUs in the node pool. The scenario already indicates that GPUs are visible, so this is not the cause of the Pending state.
- ✗
The node pool is configured with a node affinity that excludes the nodes with idle GPUs, so the scheduler cannot place the job.
Why it's wrong here
Node affinity can restrict placement, but the scenario states the node pool has 4 idle GPUs and the job requests 2. If affinity excluded those nodes, the scheduler would report no matching nodes rather than showing free GPUs in the pool. The more direct cause is a project quota limit.
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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.