NCP-AIO Installation and Deployment Practice Question
Which TWO of the following steps are essential when deploying the NVIDIA GPU Operator on a Kubernetes cluster to ensure that GPU resources are discoverable by the scheduler?
⚠ Common exam trap
Candidates often assume that installing the GPU driver is sufficient. They miss that the Kubernetes scheduler requires explicit registration via NFD and the device plugin to actually 'see' the GPU as a resource.
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
✓
Enable the node feature discovery (NFD) service in the operator configuration.
The NVIDIA GPU Operator automates the lifecycle of NVIDIA software components, including the driver, toolkit, and device plugin. By configuring the operator to deploy the GPU device plugin and the Node Feature Discovery (NFD) service, the cluster gains the ability to identify GPU hardware and advertise it as an allocatable resource. Without these, the Kubernetes scheduler cannot place pods requiring GPU resources, leading to 'Pending' status for those 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.
- ✓
Enable the node feature discovery (NFD) service in the operator configuration.
Why this is correct
NFD is essential for labeling nodes based on hardware features, such as GPU architecture. The GPU Operator uses these labels to identify nodes suitable for GPU-accelerated workloads. Without NFD, the cluster cannot dynamically detect and categorize the GPU hardware capabilities required to schedule pods accurately across the node pool.
- ✗
Manually install the NVIDIA Container Toolkit on every worker node prior to operator deployment.
Why it's wrong here
The GPU Operator is designed to automate the installation of the NVIDIA Container Toolkit. Manual installation is redundant and can lead to version conflicts or misconfigurations that impede the operator's ability to manage the software lifecycle effectively. Automation via the operator is the preferred enterprise deployment method.
- ✓
Configure the GPU device plugin to register NVIDIA-specific resources with the Kubelet.
Why this is correct
The device plugin is the bridge between the Kubernetes Kubelet and the GPU hardware. It advertises the GPU resources to the cluster, allowing the scheduler to track available capacity. Without the device plugin, Kubernetes sees no GPU resources, rendering the cluster incapable of scheduling any accelerated AI application pods.
- ✗
Modify the Kubernetes API server manifest to include the NVIDIA-specific admission controller.
Why it's wrong here
The GPU Operator does not require direct modification of the Kubernetes API server manifest for standard functionality. Admission control for GPU workloads is handled via the NVIDIA Container Runtime and the device plugin, which are orchestrated automatically by the operator without manual tampering with the central API server core configuration.
- ✗
Disable the default Kubernetes scheduler to allow the NVIDIA scheduler plugin to take over.
Why it's wrong here
The default Kubernetes scheduler is perfectly capable of handling GPU requests when provided with accurate resource information by the device plugin. Replacing the default scheduler is unnecessary for typical deployments and adds significant complexity to the cluster maintenance without providing a tangible benefit to the standard AI workload execution.
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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
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