NCP-AIO Installation and Deployment Practice Question
A media company is deploying an inference service on a Kubernetes cluster with the NVIDIA GPU Operator installed. The service pods remain in Pending with the message that no nodes have the requested nvidia.com/gpu resource, even though the GPUs are healthy and the driver loads correctly on every node. Which troubleshooting step should the engineer perform first?
⚠ Common exam trap
The trap here is jumping to driver or hardware remediation when the symptom, a healthy driver with no advertised nvidia.com/gpu resource, points squarely at the device plugin registration path.
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
✓
Check whether the NVIDIA device plugin pods are running and have registered the GPUs with kubelet.
Kubernetes learns about GPUs through the device plugin framework: the NVIDIA device plugin advertises nvidia.com/gpu for each visible GPU by registering with kubelet. When the driver is healthy but pods report no such resource, the plugin is the missing link. Checking its pod status and logs quickly reveals scheduling failures, crashes, or socket registration errors before deeper investigation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reinstall the NVIDIA driver on every node to refresh the kernel module.
Why it's wrong here
The scenario states the driver loads correctly and the GPUs are healthy, so reinstalling it addresses a problem that is not present. It also risks unnecessary node disruption. The missing nvidia.com/gpu resource points to the device plugin registration path, not to the driver.
- ✗
Increase the kubelet's pod density limit so the plugin can be scheduled.
Why it's wrong here
Pod density limits could prevent scheduling in extreme cases, but the symptom of no allocatable GPU resource across all nodes is characteristic of the device plugin not registering, not of pod capacity. Raising limits would not create the missing resource.
- ✓
Check whether the NVIDIA device plugin pods are running and have registered the GPUs with kubelet.
Why this is correct
The scheduler only sees nvidia.com/gpu capacity after the device plugin registers each GPU with kubelet through the plugin socket. If the device plugin pods are crashing or not scheduled, nodes show no GPU resource and pods stay Pending. Verifying plugin pod status and logs is the most direct first step when the driver itself is healthy.
- ✗
Enable Multi-Instance GPU mode on all GPUs so each partition advertises capacity.
Why it's wrong here
MIG changes how a GPU is partitioned and advertised, but enabling it does not fix a device plugin that is not registering at all. If the plugin is down, MIG instances also would not be advertised, so this step does not address the root cause.
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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.