NCP-AIO Installation and Deployment Practice Question
Exhibit
NAME: gpu-pod STATUS: Pending EVENTS: Warning: FailedScheduling: 0/3 nodes are available: 3 Insufficient nvidia.com/gpu.
Refer to the exhibit. An administrator attempts to deploy a GPU-based pod, but it remains in the 'Pending' state. What is the most likely cause based on the error log?
⚠ Common exam trap
Candidates often blame pod resource limits or application errors. They fail to recognize the 'Insufficient' resource error as a direct signal that the device plugin is not communicating with the Kubelet.
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 NVIDIA device plugin is not correctly advertising GPU resources.
The error 'Insufficient nvidia.com/gpu' indicates that the Kubernetes scheduler cannot find a node with available GPU resources that match the pod's request. This typically happens when the device plugin is not correctly reporting resource availability to the scheduler, or the cluster is over-provisioned. Resolving this requires ensuring the GPU Operator is running and that the device plugin has successfully registered the GPUs with the Kubelet on the worker 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.
- ✗
The pod is requesting more CPU cores than the node can provide.
Why it's wrong here
The error specifically mentions 'Insufficient nvidia.com/gpu', not CPU or memory. Kubernetes provides specific error messages for each resource type. Therefore, focusing on CPU availability is a distraction from the root cause, which is related to the GPU resource tracking mechanism and its communication with the Kubernetes scheduler.
- ✓
The NVIDIA device plugin is not correctly advertising GPU resources.
Why this is correct
The device plugin is the component responsible for telling the Kubernetes scheduler how many GPUs are available on a specific node. If this process fails or is not running, the scheduler will not see any allocatable GPU resources, resulting in the 'Insufficient' error even if physical GPUs are present.
- ✗
The node is in a 'NotReady' state due to high disk latency.
Why it's wrong here
If a node were in a 'NotReady' state, the scheduler would ignore it entirely, but the error would be different, likely indicating that there are no nodes matching the scheduler's criteria. The 'Insufficient' error implies that the scheduler evaluated the nodes but found that the GPU resource capacity was already fully utilized.
- ✗
The pod security policy prohibits the use of GPU-accelerated containers.
Why it's wrong here
Pod security policies (or Pod Security Admissions) control permissions and capabilities, not resource allocation. If a policy violation occurred, the pod would fail with a 'Forbidden' or 'Admission denied' error, not an 'Insufficient resource' error, which is strictly related to the accounting of requested versus available cluster capacity.
Visual reference
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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.