NCP-AIO Workload Management Practice Question
Which mechanism does the NVIDIA Device Plugin use to communicate GPU availability to the Kubernetes Kubelet?
⚠ Common exam trap
Candidates often guess standard REST APIs or custom webhooks instead of the gRPC-based device plugin API used by Kubernetes Kubelet.
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
✓
It uses a gRPC-based device plugin API.
The NVIDIA Device Plugin functions as a gRPC service that registers itself with the Kubelet upon startup. It performs active monitoring of the GPU nodes and reports discovered devices, including their count and health, to the Kubelet. This information is subsequently relayed to the Kubernetes API server, allowing the scheduler to make placement decisions based on real-time GPU availability and ensuring efficient workload distribution in AI clusters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It writes to a configuration file that the Kubelet watches.
Why it's wrong here
Kubernetes device plugins use a standardized gRPC interface to register with the Kubelet, rather than file-based monitoring. File watching is inefficient and error-prone for dynamic hardware discovery. The gRPC protocol provides a robust, real-time mechanism for device plugins to notify the Kubelet of hardware availability and status changes.
- ✓
It uses a gRPC-based device plugin API.
Why this is correct
The Kubernetes Device Plugin framework is built on gRPC. The NVIDIA plugin implements this API to provide the Kubelet with the necessary information about GPU resources. This standard interface allows Kubernetes to treat different hardware devices consistently while maintaining the flexibility needed for NVIDIA-specific hardware management.
- ✗
It queries the API server directly via REST calls.
Why it's wrong here
Device plugins communicate exclusively with the Kubelet via a local Unix domain socket using the device plugin protocol. They do not interact with the Kubernetes API server directly, as their role is to inform the Kubelet on the node, which then reports hardware information up to the control plane.
- ✗
It relies on the container runtime to inspect hardware.
Why it's wrong here
The container runtime (like containerd or CRI-O) is responsible for launching containers, not for discovering and reporting GPU hardware to the scheduler. That responsibility lies with the NVIDIA Device Plugin, which independently queries the host's GPU driver and reports those findings back to the cluster's orchestration system.
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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.