NCP-AIO Installation and Deployment Practice Question
Which command is used to verify that the NVIDIA GPU Operator has successfully installed the necessary components on a Kubernetes node?
⚠ Common exam trap
Candidates often confuse cluster-wide resource inspection commands like 'kubectl get nodes' or generic pod queries with operator-specific status checks, forgetting to target the dedicated namespace where the GPU Operator components reside.
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
✓
kubectl get pods -n gpu-operator
The 'kubectl get pods -n gpu-operator' command is the primary method to check the status of the operator and its managed components, such as the device plugin and driver daemonsets. This step is essential because it confirms that the operator's control loop has successfully completed the deployment, ensuring that the GPU software stack is active and ready to handle incoming AI workload scheduling requests.
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-smi check-components
Why it's wrong here
The 'nvidia-smi' command is used to query the status of the GPU hardware and the driver from within the OS, but it does not have a 'check-components' flag. This is not a valid command for verifying the installation of Kubernetes-level operators or the status of the operator's managed pods.
- ✓
kubectl get pods -n gpu-operator
Why this is correct
The GPU Operator runs in a specific namespace. Listing the pods in this namespace allows an administrator to see the status of the daemonsets, such as the driver installer and device plugin. All pods being in a 'Running' or 'Completed' state confirms a successful deployment of the operator components.
- ✗
docker inspect nvidia-gpu-operator
Why it's wrong here
The 'docker inspect' command is used to look at the details of a specific container running on the local Docker engine. It cannot be used to verify the deployment status of a Kubernetes-based operator, which is managed by the Kubelet and the cluster's orchestration API, not by the Docker daemon directly.
- ✗
kube-config verify --gpu
Why it's wrong here
There is no 'verify' command in the 'kube-config' utility. This is a fabricated command and does not exist in the Kubernetes or NVIDIA AI Enterprise toolsets. Verification must be performed by inspecting the state of the cluster's objects, such as pods, daemonsets, and custom resources, using standard kubectl commands.
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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.