NCP-AIO Installation and Deployment Practice Question
A financial services company is deploying NVIDIA AI Enterprise on a VMware vSphere cluster with NVIDIA A100 GPUs. The security team requires that GPU workloads be isolated at the hardware level, with separate memory and fault domains, to meet regulatory compliance. The company also wants to maximize GPU utilization by running multiple workloads concurrently. Which NVIDIA feature should be enabled to meet these requirements?
⚠ Common exam trap
Many candidates confuse time-sliced vGPU or other GPU sharing technologies with true hardware partitioning, which only MIG provides on A100 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) is the only feature that provides hardware-level partitioning of an A100 GPU into isolated instances with dedicated memory, cache, and compute resources. This meets the need for fault domain separation and regulatory compliance while enabling concurrent execution of multiple workloads, thereby maximizing utilization. Other options either share resources without isolation or address different concerns like data transfer or inter-GPU communication.
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 NVLink with SHARP
Why it's wrong here
NVLink with SHARP (Scalable Hierarchical Aggregation and Reduction Protocol) enhances inter-GPU communication and collective operations. It is used for scaling workloads across multiple GPUs, not for partitioning a single GPU into isolated instances. It does not provide hardware-level isolation or allow multiple independent workloads on one GPU. Therefore, it does not satisfy the stated compliance and utilization requirements.
- ✗
NVIDIA vGPU with time-sliced scheduling
Why it's wrong here
NVIDIA vGPU with time-slicing shares the GPU among virtual machines by switching execution contexts, but it does not provide hardware-level isolation of memory or fault domains. A fault in one workload can affect others. While vGPU is suitable for many virtualization use cases, it does not meet the strict isolation requirements described. Therefore, it is not the correct solution for this compliance-driven scenario.
- ✗
NVIDIA GPUDirect Storage
Why it's wrong here
GPUDirect Storage enables direct memory access between GPU memory and storage devices, bypassing the CPU and system memory. It improves data transfer performance for I/O-intensive workloads but does not provide GPU partitioning, isolation, or concurrent execution capabilities. It is unrelated to the requirement of hardware-level isolation and utilization maximization through partitioning. Thus, it is not the appropriate feature here.
- ✓
NVIDIA Multi-Instance GPU (MIG)
Why this is correct
MIG partitions an A100 GPU into up to seven independent instances, each with dedicated memory, cache, and compute resources. This provides hardware-level isolation and fault domain separation, satisfying regulatory requirements for workload isolation. It also allows multiple workloads to run concurrently on a single GPU, maximizing utilization. MIG is the correct choice for this scenario because it uniquely combines isolation and concurrency on A100 hardware.
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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.