NCP-AIO Installation and Deployment Practice Question
Which NVIDIA technology enables the partitioning of a single physical GPU into multiple independent instances for use by different virtual machines or containers?
⚠ Common exam trap
Candidates often confuse MIG with vGPU or Time-Slicing, failing to specify that MIG is the unique hardware-level partitioning technology for NVIDIA GPUs.
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
✓
NVIDIA Multi-Instance GPU (MIG).
NVIDIA Multi-Instance GPU (MIG) technology is the correct answer. It allows a single GPU to be securely partitioned at the hardware level, providing guaranteed QoS and isolation for different workloads. This is essential for maximizing GPU utilization in enterprise AI, as it enables the co-location of small inference tasks alongside larger training workloads on a single piece of high-end hardware.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
NVIDIA GPUDirect Storage.
Why it's wrong here
GPUDirect Storage is an I/O path optimization that allows data to be transferred directly from storage to GPU memory. It has no role in partitioning GPU compute resources or memory for multi-tenancy. It is strictly focused on optimizing the I/O throughput for data-heavy AI training and inference tasks.
- ✓
NVIDIA Multi-Instance GPU (MIG).
Why this is correct
MIG enables hardware-level partitioning of the GPU, allowing each instance to have its own compute cores, memory, and cache. This provides robust isolation and performance guarantees for multiple applications or users, ensuring that one workload does not adversely impact the performance of another co-located on the same device.
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NVIDIA NVLink.
Why it's wrong here
NVLink is a high-speed interconnect technology used to link multiple GPUs together to share memory and data more efficiently. It is designed to scale across multiple physical devices rather than dividing a single physical device into smaller, independent units for concurrent workload execution.
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NVIDIA vGPU Profiles.
Why it's wrong here
While vGPU allows for sharing a GPU among virtual machines, MIG provides a more granular hardware-level partitioning that includes dedicated compute and memory pathways. vGPU is often implemented at the hypervisor level, whereas MIG is a hardware-integrated feature that provides superior performance isolation and security for AI workloads.
About these practice questions
This NCP-AIO question is part of Courseiva's 309-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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.