NCP-AIO Workload Management Practice Question
Exhibit
Error: Failed to create pod: pods "gpu-job" is forbidden: failed quota: gpu-quota: must specify limits for nvidia.com/gpu
Refer to the exhibit. An administrator is attempting to deploy a job to a namespace with a ResourceQuota defined. What is the cause of this error?
⚠ Common exam trap
Candidates often assume ResourceQuota errors stem from cluster-wide node exhaustion, missing the fact that namespace-level policies explicitly require explicit GPU resource limits in the pod manifest.
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 pod manifest is missing the required GPU resource limits.
The error indicates that the namespace has a ResourceQuota enforcing that all pods must specify GPU limits, but the submitted pod manifest lacks a 'resources.limits.nvidia.com/gpu' entry. In multi-tenant environments, ResourceQuotas are essential for preventing a single user from consuming the entire GPU capacity. The manifest must include a valid GPU limit to satisfy the namespace policy, ensuring the cluster remains balanced across different organizational teams.
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 GPU driver version is incompatible with the quota controller.
Why it's wrong here
ResourceQuotas are handled by the Kubernetes API server and controller manager, not the GPU driver. The error is purely related to admission control policies in Kubernetes. The driver version does not influence how quotas are evaluated; the error is clearly a violation of a policy defined for that namespace.
- ✓
The pod manifest is missing the required GPU resource limits.
Why this is correct
The error message explicitly states that limits for 'nvidia.com/gpu' must be specified. This is a common requirement in environments where quotas are implemented to ensure fair scheduling. Without these limits, the admission controller rejects the pod because it cannot account for the GPU usage against the namespace quota.
- ✗
The cluster is out of available GPU capacity.
Why it's wrong here
If the cluster were out of capacity, the error would manifest as a scheduling failure (Pending status) rather than a rejection at the API server level. Admission control errors regarding 'forbidden' actions indicate policy violations rather than physical hardware availability issues within the cluster nodes themselves.
- ✗
The NVIDIA Device Plugin is not running in the namespace.
Why it's wrong here
The device plugin's presence does not impact the API server's ability to enforce resource quotas. A quota error is a configuration issue within the namespace settings and the submitted YAML manifest. The device plugin is only relevant after the pod passes the admission controller and begins the scheduling phase.
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.