NCP-AIO Installation and Deployment Practice Question
A system administrator is installing NVIDIA AI Enterprise on a Kubernetes cluster that will use Multi-Instance GPU (MIG) on A100 GPUs. The administrator wants to ensure that MIG instances are properly exposed as schedulable resources. Which action must be taken after enabling MIG mode on the GPUs?
⚠ Common exam trap
The trap here is assuming that enabling MIG mode on the GPU is sufficient, when Kubernetes also requires the device plugin to advertise MIG resources.
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
✓
Install the NVIDIA GPU Operator with the MIG strategy set to 'mixed' and configure the device plugin to advertise MIG resources.
To expose MIG instances as schedulable resources in Kubernetes, the NVIDIA GPU Operator must be installed with the MIG strategy configured (e.g., 'mixed' or 'single'). The Operator's device plugin then discovers and advertises each MIG instance as a resource. This allows pods to request specific MIG profiles via resource limits, enabling efficient scheduling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually create Kubernetes custom resources for each MIG instance using the NVIDIA MIG Manager.
Why it's wrong here
The NVIDIA MIG Manager is used to configure MIG profiles on the host, but it does not directly create Kubernetes custom resources for scheduling. The GPU Operator's device plugin handles resource advertisement. Manually creating custom resources is not the standard method and would not integrate with the scheduler's resource model.
- ✗
Set the environment variable NVIDIA_MIG_CONFIG_DEVICES to 'all' on the kubelet and restart the kubelet service.
Why it's wrong here
The NVIDIA_MIG_CONFIG_DEVICES environment variable is used by the NVIDIA container runtime to specify which MIG devices to configure, but it does not advertise MIG resources to Kubernetes. The kubelet relies on the device plugin for resource advertisement. Setting this variable alone would not make MIG instances schedulable.
- ✗
Deploy a separate device plugin for each MIG instance using a DaemonSet with node affinity to the specific GPU.
Why it's wrong here
Deploying a separate device plugin per MIG instance is not feasible or supported. The GPU Operator's device plugin is designed to handle all MIG instances on a node. Using multiple DaemonSets would cause conflicts and is not a scalable or recommended approach. The standard method is a single device plugin that advertises all MIG resources.
- ✓
Install the NVIDIA GPU Operator with the MIG strategy set to 'mixed' and configure the device plugin to advertise MIG resources.
Why this is correct
The GPU Operator supports MIG by deploying a device plugin that advertises MIG instances as resources. Setting the MIG strategy to 'mixed' allows both MIG and non-MIG GPUs in the cluster. The device plugin then exposes each MIG instance as a schedulable resource, enabling pods to request specific MIG profiles. This is the correct way to integrate MIG with Kubernetes scheduling.
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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.