NCP-AIO Installation and Deployment Practice Question
An administrator is deploying NVIDIA AI Enterprise on a bare-metal cluster. The workload requires full GPU isolation with minimal latency. Which configuration should the administrator select to achieve this goal?
⚠ Common exam trap
Candidates often incorrectly choose virtualization or container-only solutions, failing to recognize that 'bare-metal' and 'GPUDirect RDMA' are the specific, non-negotiable requirements for minimizing latency in high-performance computing environments.
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
✓
Implement bare-metal installation with NVIDIA GPUDirect RDMA enabled.
GPU Direct and bare-metal deployments are essential for latency-sensitive workloads. By avoiding hypervisor overhead, the administrator ensures direct path access to the GPU memory and interconnects. This configuration is critical in high-performance computing environments where jitter and interrupt latency can degrade model training performance. Selecting the right deployment mode is a foundational step in AI Operations to ensure optimal hardware utilization and predictable execution times for deep learning models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy using NVIDIA vGPU on a KVM hypervisor.
Why it's wrong here
Hypervisors introduce scheduling overhead and emulation layers that increase latency compared to bare-metal. While vGPU provides excellent flexibility and resource partitioning for general AI tasks, it is not the optimal choice when the primary objective is reaching the absolute lowest latency thresholds for high-frequency compute workloads.
- ✗
Configure the system using NVIDIA License System (NLS) in disconnected mode.
Why it's wrong here
License system configuration manages software entitlement but does not affect the underlying hardware performance or latency characteristics of the GPU. While essential for software compliance and feature activation, it is an administrative task rather than a performance-tuning mechanism for low-latency hardware interaction.
- ✓
Implement bare-metal installation with NVIDIA GPUDirect RDMA enabled.
Why this is correct
GPUDirect RDMA allows direct memory access between the GPU and third-party devices such as NICs, effectively bypassing the host CPU. This architecture eliminates unnecessary data copies and context switches, providing the lowest possible latency for high-speed AI data pipelines and distributed training environments on physical infrastructure.
- ✗
Utilize containerized GPU passthrough with a standard Docker runtime.
Why it's wrong here
Standard Docker GPU passthrough lacks the specific optimizations for NVLink and RDMA interconnects required for specialized low-latency setups. While functional for general inference, it does not provide the same degree of performance tuning and hardware-level isolation as a dedicated bare-metal deployment optimized for high-bandwidth data transfers.
About these practice questions
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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.