NCP-AIO Installation and Deployment Practice Question
Which component is responsible for exposing the GPU as a schedulable resource in a Kubernetes cluster?
⚠ Common exam trap
Candidates often select the NVIDIA Container Toolkit or GPU Operator instead of the specific component that directly advertises resources to the kubelet 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 Device Plugin
The Kubernetes device plugin is the interface that allows the kubelet to communicate with the GPU. It advertises the number of available GPUs on each node to the Kubernetes API server. When a pod requests a GPU, the scheduler uses this information to place the workload on the correct node. Without this plugin, Kubernetes is unaware of the GPU hardware, making it impossible to manage and allocate GPU resources for containerized workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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NVIDIA Container Toolkit
Why it's wrong here
The Container Toolkit provides the runtime hooks to run GPU-accelerated containers, but it does not perform the scheduling or resource advertisement required by Kubernetes. It is a lower-level tool that operates once the container is already being scheduled, so it cannot serve as the resource plugin.
- ✓
NVIDIA Device Plugin
Why this is correct
The device plugin is the crucial component for Kubernetes integration. It polls the host for GPU status and reports capacity to the kubelet, which then tells the scheduler. This bridge is essential for enabling the 'nvidia.com/gpu' resource type, which allows pods to request GPUs as first-class resources.
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NVIDIA DCGM Exporter
Why it's wrong here
The exporter is dedicated to telemetry and metrics, not resource scheduling. It provides data for Prometheus but does not interact with the Kubernetes scheduling loop or provide resource advertisements to the API server. Using it for scheduling would be an incorrect application of its telemetry-focused design.
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The Kubernetes Scheduler.
Why it's wrong here
While the scheduler makes the final placement decision, it relies on information provided by the device plugin. The scheduler itself does not have built-in knowledge of specialized hardware like GPUs; it expects the device plugin to provide the resource capacity information it needs to make an informed decision.
About these practice questions
Courseiva writes every NCP-AIO question from scratch — 309 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.