NCP-AIO Workload Management Practice Question
A data engineering team runs nightly batch inference on a Kubernetes cluster with NVIDIA GPUs. Jobs sometimes fail because two pods are scheduled onto the same physical GPU and one exhausts framebuffer memory. The team wants each pod to receive an isolated slice of a GPU with dedicated memory. Which NVIDIA feature should they enable?
⚠ Common exam trap
A common mix-up: candidates confuse containerization or communication tooling, which makes GPUs usable, with partitioning features that actually isolate GPU memory.
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)
The team needs hardware-level isolation with dedicated memory per workload. Multi-Instance GPU partitions a physical GPU into independent instances, each with its own memory and compute, and the device plugin can expose those instances as discrete resources. GPUDirect Storage, NCCL, and the Container Toolkit each address I/O paths, communication, or containerization rather than partitioning a GPU among tenants.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Multi-Instance GPU (MIG)
Why this is correct
MIG partitions a single physical GPU into multiple independent instances, each with its own dedicated memory and compute slices. Because the memory is hardware-partitioned, one instance cannot consume another's framebuffer, which directly prevents the failure the team is seeing. The device plugin can then advertise each MIG instance as a schedulable resource so pods receive isolated slices.
- ✗
NVIDIA Collective Communications Library (NCCL)
Why it's wrong here
NCCL implements collective communication primitives such as all-reduce for multi-GPU and multi-node workloads. It is essential for distributed training performance but is a communication library, not a resource partitioning mechanism. It cannot create isolated GPU slices, so it would not prevent co-located pods from competing for memory on a single device.
- ✗
GPUDirect Storage
Why it's wrong here
GPUDirect Storage provides a direct data path between GPU memory and local or remote storage, bypassing host memory for I/O. It improves data loading throughput for training and inference pipelines but does nothing to partition compute or memory on a GPU. Enabling it would not stop two pods from sharing one device or from exhausting framebuffer memory, so it does not solve the stated problem.
- ✗
NVIDIA Container Toolkit
Why it's wrong here
The NVIDIA Container Toolkit makes GPUs visible inside containers by mounting driver libraries and device nodes. It is required for any containerized GPU workload but provides no partitioning or memory isolation. Two containers on the same node would still see the same device and could still exhaust its memory, leaving the original failure mode unresolved.
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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.