NCP-AIO Workload Management Practice Question
An administrator manages a Kubernetes cluster where the NVIDIA GPU Operator has deployed the device plugin and MIG Manager. A tenant wants to run several small inference services that each need only a fraction of a GPU, isolated from other tenants' memory and fault domains. The administrator decides to use Multi-Instance GPU mode. Which TWO actions must be performed to make MIG-backed GPU resources schedulable to those pods? (Choose two.)
⚠ Common exam trap
The trap here is treating a RuntimeClass or ResourceQuota as the mechanism that creates MIG slices, when the essential pair is MIG geometry configuration plus requesting the advertised profile-specific extended resource.
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
✓
Enable MIG mode on the physical GPU and define a MIG profile/geometry via the MIG Manager configuration
MIG-backed scheduling requires two coordinated steps: enabling MIG mode and applying a geometry through the MIG Manager so instances exist, and having pods request the specific profile resource name that the device plugin advertises, such as nvidia.com/mig-1g.5gb. Together these produce isolated GPU slices with dedicated memory and fault domains for each tenant's inference service.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Install the NVIDIA Network Operator and enable RDMA device advertisement on the node
Why it's wrong here
The Network Operator manages high-speed networking components such as Mellanox NICs and RDMA for distributed training, not GPU partitioning. Enabling RDMA advertisement has no effect on MIG instance creation or on how the device plugin names GPU slice resources, so it cannot make MIG-backed resources schedulable to inference pods.
- ✓
Enable MIG mode on the physical GPU and define a MIG profile/geometry via the MIG Manager configuration
Why this is correct
MIG mode must be enabled on the GPU, and the MIG Manager in the GPU Operator applies a geometry configuration that carves the device into instances with dedicated memory and fault isolation. Without an applied profile, no MIG instances exist and the node advertises no MIG resources, so scheduling cannot succeed regardless of pod specification.
- ✓
Request the MIG device in the pod spec using the extended resource name advertised by the device plugin, such as nvidia.com/mig-1g.5gb
Why this is correct
Once MIG instances exist, the NVIDIA device plugin advertises each profile as an extended resource. Pods must request that exact resource name, for example nvidia.com/mig-1g.5gb, so the scheduler and kubelet can allocate a specific instance. Requesting a generic nvidia.com/gpu would not map to the intended isolated MIG slice.
- ✗
Create a RuntimeClass that points to the nvidia-container-runtime and reference it from each inference pod
Why it's wrong here
A RuntimeClass selecting nvidia-container-runtime is needed for GPU access generally, but it does not create or advertise MIG instances. It is orthogonal to MIG geometry and resource naming. Configuring it alone leaves the node without MIG resources, so pods still cannot schedule against a MIG profile even though the runtime is correctly selected.
- ✗
Apply a ResourceQuota that limits nvidia.com/gpu to zero in the tenant namespace
Why it's wrong here
Setting the GPU quota to zero would actively prevent any GPU or MIG resource consumption in that namespace, which is the opposite of the goal. ResourceQuota governs aggregate consumption and does not create MIG instances or advertise profile-specific resource names, so it cannot enable scheduling of MIG-backed pods.
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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
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