NCP-AIO Workload Management Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Enable NVIDIA Multi-Instance GPU (MIG) on supported GPUs and expose the MIG instances as schedulable resources.

  Why this is correct

  MIG partitions a supported GPU into isolated instances, each with dedicated memory and compute slices. When the GPU Operator exposes these instances through the device plugin, each MIG instance is advertised as its own resource, so multiple pods can run concurrently on one physical card with hardware-level isolation. This is a supported way to share a single GPU across pods.

• ✓

  Configure time-slicing in the device plugin so several pods share a GPU through interleaved execution.

  Why this is correct

  Time-slicing, configured through a device plugin ConfigMap, advertises a configurable number of replicas per physical GPU. The scheduler then places multiple pods on the same card, and the runtime interleaves their work. This is a documented sharing mode that lets more pods consume one GPU than there are physical devices, making it a valid configuration for this scenario.

• ✗

  Add a node selector that allows multiple pods to bind to the same nvidia.com/gpu resource slot.

  Why it's wrong here

  A node selector only filters which nodes a pod may run on; it does not change how GPU resources are counted or allocated. The scheduler still treats each nvidia.com/gpu unit as consumable by exactly one pod unless a sharing mode is configured. Node selectors therefore cannot enable multiple pods to occupy the same physical GPU slot.

• ✗

  Create a ResourceQuota that counts GPU requests as fractional values such as 0.5 per pod.

  Why it's wrong here

  Kubernetes does not support fractional extended resources like nvidia.com/gpu; requests must be whole integers unless a sharing mechanism changes how devices are advertised. A ResourceQuota merely enforces limits on those integer requests. Defining fractional values in quota will not let two pods share one GPU and will likely be rejected or ignored.

• ✗

  Raise the GPU Operator's driver version so the device plugin reports extra virtual GPUs per card.

  Why it's wrong here

  Driver versions do not create additional schedulable GPU resources. The number of advertised devices is governed by the device plugin's configuration, such as MIG or time-slicing settings, not by the driver release. Upgrading the driver might fix compatibility issues, but it will not by itself allow more pods to share a single physical GPU.