NCP-AIO Workload Management Practice Question
When managing workloads on NVIDIA DGX systems, what is the primary role of the NVIDIA device plugin in the Kubernetes ecosystem?
⚠ Common exam trap
Candidates often confuse the device plugin with the container runtime. The plugin only handles hardware advertisement and discovery, not the injection of CUDA libraries or driver communication into the container.
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
✓
To monitor the health status of GPUs and advertise them to the Kubernetes scheduler.
The NVIDIA device plugin acts as an interface between Kubernetes and the NVIDIA drivers, enabling the scheduler to advertise and allocate GPUs as first-class resources. It is fundamental to GPU workload management because it informs the kubelet about the availability, health, and count of GPUs on the node, ensuring that pods requesting GPUs are placed on nodes with sufficient capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To compile CUDA code for specific GPU architectures during the job scheduling process.
Why it's wrong here
The device plugin does not handle compilation. Compilation is a build-time task managed by the container image or build pipelines. The plugin's only responsibility is to report resource availability to the scheduler and handle the assignment of devices to containers at runtime.
- ✓
To monitor the health status of GPUs and advertise them to the Kubernetes scheduler.
Why this is correct
The device plugin is the bridge that allows Kubernetes to 'see' the GPUs. It queries the NVIDIA driver for device information, reports healthy devices to the API server, and manages the lifecycle of GPU assignments, ensuring the scheduler only places GPU-dependent pods on nodes that can support them.
- ✗
To automatically optimize neural network hyper-parameters for faster training throughput.
Why it's wrong here
Hyper-parameter optimization occurs at the application or framework level, not the infrastructure plugin level. The device plugin is agnostic to the workload running inside the container; its role is strictly limited to managing physical resource allocation and visibility for the Kubernetes cluster manager.
- ✗
To replace the need for the NVIDIA Container Toolkit in the container runtime environment.
Why it's wrong here
The device plugin and the NVIDIA Container Toolkit serve complementary roles. The toolkit enables the container to access host drivers and libraries, while the plugin informs the orchestrator about the existence of the hardware. Both are required for a functional GPU-enabled Kubernetes environment.
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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.