NCP-AIO Workload Management Practice Question
A platform team runs an NVIDIA AI cluster with the GPU Operator deployed. Users submit jobs directly with kubectl and frequently request whole GPUs even when their notebooks only need a fraction of one. The team wants Kubernetes itself to admit and queue jobs based on GPU demand without users changing their manifests. Which component should the team deploy to meet this requirement?
⚠ Common exam trap
The trap here is assuming the GPU Operator itself performs scheduling or admission, when it only installs and manages the GPU software stack.
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 KAI Scheduler
The requirement is for Kubernetes to make admission and queueing decisions based on GPU demand while users submit normal manifests. KAI Scheduler is the NVIDIA component that provides queue-based, gang-aware scheduling for AI workloads and understands fractional GPU requests, so jobs wait rather than fail. The GPU Operator, MIG Manager, and DCGM Exporter each address installation, partitioning, or observability rather than scheduling admission.
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 MIG Manager configured for every GPU
Why it's wrong here
MIG Manager partitions a physical GPU into isolated instances and is useful for deterministic isolation, but it does not queue or admit jobs on behalf of Kubernetes. It also changes the resource topology, requiring MIG-aware resource names in pod specs. It cannot enforce admission based on current demand, so it fails the requirement that users keep submitting unchanged manifests.
- ✗
NVIDIA GPU Operator with the device plugin enabled
Why it's wrong here
The GPU Operator installs and manages the device plugin, driver, and related components, but the device plugin only advertises whole-GPU resources such as nvidia.com/gpu to the kubelet. It does not implement queueing or admission decisions, and it cannot grant a fraction of a GPU. Enabling it therefore does nothing to solve oversubscription or job admission for this scenario.
- ✗
NVIDIA DCGM Exporter with Prometheus alerting
Why it's wrong here
DCGM Exporter collects GPU telemetry and exposes it as Prometheus metrics, which is valuable for monitoring utilization and health. However, metrics collection is observational only; it cannot admit, reject, or queue a pod. Alerting on high demand would notify an operator after the fact, but Kubernetes scheduling decisions would remain unchanged, so this does not meet the requirement.
- ✓
NVIDIA KAI Scheduler
Why this is correct
KAI Scheduler is NVIDIA's Kubernetes-native scheduler for AI workloads. It plugs into the cluster as a secondary scheduler and performs gang scheduling, queueing, and GPU fraction accounting, so jobs that request partial GPUs or exceed current capacity wait in a queue instead of being rejected. Because admission and queueing happen inside the scheduler, users keep submitting standard manifests and do not need to learn new tooling.
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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.