NCP-AIO Installation and Deployment Practice Question
A DevOps engineer is deploying the NVIDIA GPU Operator on a Kubernetes cluster that uses containerd as the container runtime. The engineer notices that the Operator's validation pod fails with an error indicating that the NVIDIA container runtime is not configured. Which action should the engineer take to resolve this?
⚠ Common exam trap
The trap here is thinking manual installation or switching runtimes is needed, when the GPU Operator is designed to handle runtime configuration automatically.
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
✓
Ensure that the GPU Operator's container-toolkit daemonset is enabled and has the necessary permissions to modify the container runtime configuration.
The GPU Operator uses a container-toolkit daemonset to install and configure the NVIDIA Container Toolkit on each node, including modifying the containerd configuration. If this daemonset is disabled or lacks privileges, the runtime won't be set up, causing validation failures. Ensuring the daemonset is enabled and has proper permissions resolves the issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually install the NVIDIA Container Toolkit on all nodes and set the default runtime to nvidia.
Why it's wrong here
While manually installing the toolkit might work, the GPU Operator is designed to automate this. Manually configuring the runtime could conflict with the Operator's management. The Operator expects to deploy and configure the toolkit itself. The correct approach is to allow the Operator to manage the runtime configuration, not to bypass it.
- ✗
Switch the cluster to use Docker as the container runtime, as the GPU Operator only supports Docker.
Why it's wrong here
The GPU Operator supports both containerd and Docker. Switching to Docker is unnecessary and disruptive. The error is likely due to misconfiguration of the toolkit daemonset, not an unsupported runtime. Docker is not required; containerd is fully supported when properly configured by the Operator.
- ✓
Ensure that the GPU Operator's container-toolkit daemonset is enabled and has the necessary permissions to modify the container runtime configuration.
Why this is correct
The GPU Operator deploys a container-toolkit daemonset that installs and configures the NVIDIA Container Toolkit. If this daemonset is disabled or lacks permissions (e.g., privileged access), it cannot modify the containerd configuration. Enabling it and ensuring proper RBAC and security context allows the Operator to set up the runtime, resolving the validation error.
- ✗
Disable the validation pod in the GPU Operator's Helm chart to suppress the error.
Why it's wrong here
Disabling the validation pod hides the symptom but does not fix the underlying issue: the container runtime is not configured for NVIDIA GPUs. Without proper runtime configuration, GPU workloads will fail. The validation pod is a diagnostic tool; disabling it prevents detection of misconfigurations and is not a solution.
Visual reference
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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.