NCP-AIO Workload Management Practice Question
A platform engineer is preparing an NVIDIA-accelerated Kubernetes cluster for a new team that will submit PyTorch training jobs. The team wants jobs to request GPUs without hardcoding device indices. Which Kubernetes resource should the engineer ensure is installed and healthy so pods can request nvidia.com/gpu resources?
⚠ Common exam trap
The trap here is equating GPU driver or container runtime installation with resource advertisement, when only the device plugin registers nvidia.com/gpu with the Kubernetes scheduler.
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 GPU Operator with the device plugin component enabled
Kubernetes learns about specialized hardware through device plugins that advertise extended resources. The NVIDIA device plugin, managed by the GPU Operator, publishes nvidia.com/gpu counts so the scheduler can allocate GPUs to pods. With it healthy, PyTorch jobs can declare a GPU request and receive an assigned device without the user specifying a physical index.
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 Container Toolkit installed on each worker node
Why it's wrong here
The NVIDIA Container Toolkit enables container runtimes to expose GPUs to containers, which is necessary for GPU access but does not by itself register GPU resources with the Kubernetes API. Without the device plugin, the scheduler has no knowledge of nvidia.com/gpu capacity, so pods still cannot request GPUs through the scheduler.
- ✗
NVIDIA DCGM Exporter deployed as a DaemonSet
Why it's wrong here
DCGM Exporter collects GPU telemetry such as utilization, temperature, and memory usage and exposes it as Prometheus metrics. It is a monitoring component, not a resource advertisement mechanism. Deploying it helps observability but does nothing to let a pod declare a GPU request, so it is not the resource the engineer needs for this scenario.
- ✗
NVIDIA Network Operator with RDMA shared device plugin
Why it's wrong here
The Network Operator provisions high-speed networking components such as RDMA and GPUDirect for distributed communication. It does not advertise GPU compute resources to the Kubernetes scheduler. Installing it would improve inter-node bandwidth for multi-node training but would not enable pods to request nvidia.com/gpu, so it does not satisfy the requirement.
- ✓
NVIDIA GPU Operator with the device plugin component enabled
Why this is correct
The NVIDIA device plugin, deployed by the GPU Operator, registers nvidia.com/gpu as an extended resource and advertises the count of available GPUs on each node. Without it, pods cannot request GPUs by resource name. Ensuring the operator and its device plugin are healthy is the correct step to let PyTorch jobs request GPUs abstractly rather than by device index.
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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.