NCP-AIO Workload Management Practice Question
Which NVIDIA technology allows for partitioning a single physical GPU into multiple independent instances, each with dedicated compute and memory resources for smaller workloads?
⚠ Common exam trap
Candidates often confuse MIG with general virtualization or container-based GPU sharing, failing to realize MIG is a hardware-level partitioning technology specific to Ampere and newer NVIDIA architectures for isolated, secure workloads.
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
✓
Multi-Instance GPU (MIG)
Multi-Instance GPU (MIG) is a feature in NVIDIA A100 and H100 architectures that enables the partitioning of a physical GPU into up to seven separate instances. This is essential for AI Ops to maximize resource utilization by running multiple inference tasks on a single GPU without interference, ensuring Quality of Service for each instance while preventing a single process from consuming all hardware 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.
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CUDA Streams
Why it's wrong here
CUDA Streams are software-level queues for managing concurrency within a single GPU context. They allow for overlapping kernel execution but do not provide hardware-level isolation or resource partitioning between different workloads, meaning one task can still negatively impact the performance of others through shared memory pressure.
- ✓
Multi-Instance GPU (MIG)
Why this is correct
MIG enables the hardware-level partitioning of GPUs. By creating distinct instances with dedicated compute, memory, and cache, it provides fault isolation and guaranteed performance, which is vital for modern multi-tenant AI clusters that need to support varying workload sizes efficiently without resource contention or cross-tenant interference.
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NVIDIA Collective Communications Library (NCCL)
Why it's wrong here
NCCL is a library for implementing multi-GPU and multi-node communication primitives. It is optimized for bandwidth and latency in distributed training tasks, not for slicing or partitioning a single physical GPU's resources for the purpose of hosting multiple independent workloads simultaneously.
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NVIDIA Container Runtime
Why it's wrong here
The NVIDIA Container Runtime facilitates the creation of GPU-enabled containers by mapping the host drivers into the container environment. While it is necessary for running AI workloads, it lacks the hardware-level partitioning capabilities that define how the physical GPU silicon is allocated to separate processes.
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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.