NCP-AIO Troubleshooting and Optimization Practice Question
Exhibit
{
"policy": "restrict_gpu_access",
"targets": ["user_a"],
"max_concurrent_jobs": 2,
"resource_quota": {
"gpu_memory": "16GB"
}
}Refer to the exhibit. An administrator notices that 'user_a' is consistently hitting resource limits despite having sufficient total system GPU memory. Based on the policy JSON, what is the cause?
⚠ Common exam trap
Candidates often look at the total cluster capacity rather than the specific user-level quota. They assume the user can access all available VRAM, ignoring the scheduler's hard limit policy.
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 user is exceeding the 16GB VRAM limit.
The policy specifies a hard resource quota of '16GB' for the user. Even if the total system memory is higher, the scheduler enforces this limit per user. When the user's workload attempts to allocate more than 16GB of VRAM, the job will fail or throttle. This is a common method for preventing single users from starving the entire cluster of shared resources.
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 system has reached the max_concurrent_jobs limit.
Why it's wrong here
While the policy limits concurrent jobs to two, the stem specifically mentions hitting 'resource limits' related to memory. The memory constraint is an independent limit defined in the policy. The error reported is clearly tied to the resource quota configuration, not the job count limit defined in the JSON.
- ✓
The user is exceeding the 16GB VRAM limit.
Why this is correct
The JSON clearly defines a resource quota of '16GB'. When the user's training or inference job requests memory beyond this allocated limit, the enforcement mechanism blocks further allocation. This is a deliberate configuration to ensure fair resource distribution among multiple users in a shared GPU cluster environment.
- ✗
The NVIDIA driver is blocking the user's access.
Why it's wrong here
The NVIDIA driver does not enforce per-user memory quotas; this is handled by a higher-level orchestration or scheduling layer. The JSON policy indicates an management-level configuration file, not a driver-level limitation. The error is due to the policy quota, not a restriction imposed by the underlying low-level hardware driver.
- ✗
The GPU memory is fragmented across the nodes.
Why it's wrong here
Fragmentation can cause issues, but in this context, the specific JSON quota is the bottleneck being applied. The administrator is looking at a policy-driven limit. Fragmentation is a low-level memory management issue that would manifest differently than a hard limit defined by an administrative resource policy configuration file.
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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.