NCP-AIO Workload Management Practice Question
A platform team is preparing a Kubernetes cluster for AI workloads and wants the GPU device plugin, driver containers, and monitoring components deployed and kept in sync automatically on every GPU node. Which component should be installed to achieve this?
⚠ Common exam trap
It's easy for candidates to confuse the NVIDIA Container Toolkit, which only wires the runtime for GPU access, with the GPU Operator that manages the whole stack automatically.
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
The NVIDIA GPU Operator is purpose-built to manage the entire GPU software stack on Kubernetes nodes through automated reconciliation. It deploys the driver, container toolkit configuration, device plugin, and DCGM-based monitoring, so GPU resources are advertised and maintained without manual per-node work. The Network Operator addresses networking, while DCGM and the Container Toolkit are individual pieces the Operator already orchestrates.
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 Network Operator
Why it's wrong here
The NVIDIA Network Operator manages high-speed networking components such as RDMA and GPUDirect for distributed training, not the GPU driver or device plugin stack. Deploying it alone would not enable Kubernetes to advertise nvidia.com/gpu resources, and GPU pods would remain unschedulable because no device plugin would be present.
- ✗
NVIDIA DCGM standalone on each node
Why it's wrong here
DCGM collects telemetry and health data from GPUs but does not install drivers or register GPU resources with the kubelet. Running DCGM alone leaves the cluster without a device plugin, so the scheduler cannot allocate nvidia.com/gpu, and GPU workloads would fail to start.
- ✗
NVIDIA Container Toolkit installed manually on each node
Why it's wrong here
The NVIDIA Container Toolkit enables containers to access GPUs by configuring the container runtime, but it must be installed per node and does not include lifecycle management, device advertisement, or monitoring. It solves only part of the problem and requires manual upkeep, contradicting the goal of automatic synchronized deployment.
- ✓
NVIDIA GPU Operator
Why this is correct
The NVIDIA GPU Operator uses the Operator pattern to deploy and manage the GPU driver, container runtime hooks, device plugin, DCGM exporter, and related components as DaemonSets. It continuously reconciles node state, so new GPU nodes are automatically provisioned with the full software stack, which directly matches the requirement for automatic, synchronized deployment.
Visual reference
About these practice questions
This NCP-AIO question is part of Courseiva's 309-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.