NCP-AIO Installation and Deployment Practice Question
An AI operations engineer is preparing a Kubernetes cluster to run GPU-accelerated workloads using the NVIDIA GPU Operator. The cluster nodes already have NVIDIA data center GPUs installed and the NVIDIA driver is pre-installed on the host. The engineer wants to use the GPU Operator to manage the container toolkit, device plugin, and monitoring components but must avoid the Operator managing or upgrading the driver. Which configuration should be applied to the GPU Operator deployment?
⚠ Common exam trap
The trap here is assuming that specifying a driver version or disabling the toolkit will prevent driver installation, when only the driver.enabled flag controls driver management.
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
✓
Set the driver.enabled parameter to false in the GPU Operator's Helm chart values.
When the NVIDIA driver is already present on host nodes and should remain externally managed, the GPU Operator must be configured to not deploy its own driver container. The driver.enabled=false Helm value achieves this by skipping the driver daemonset while still deploying other components like the container toolkit, device plugin, and DCGM exporter. This preserves the existing driver and avoids conflicts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the NVIDIA GPU Operator with the --set toolkit.enabled=false option to prevent driver installation.
Why it's wrong here
The toolkit.enabled parameter controls whether the NVIDIA Container Toolkit is deployed, not the driver. Setting toolkit.enabled=false would prevent the container toolkit from being installed, which would break GPU container runtime support. This does not address the requirement to avoid driver management and would actually disable a necessary component for running GPU-accelerated containers.
- ✓
Set the driver.enabled parameter to false in the GPU Operator's Helm chart values.
Why this is correct
Setting driver.enabled=false instructs the GPU Operator to skip deploying the driver container and instead rely on the pre-installed host driver. This is the supported method for clusters where the driver is managed externally, such as via the node's package manager. The Operator will still deploy the container toolkit, device plugin, and DCGM exporter, allowing full GPU scheduling and monitoring without touching the driver.
- ✗
Install the GPU Operator with the --set operator.driverVersion=latest flag to pin the driver version.
Why it's wrong here
The operator.driverVersion parameter does not exist in the GPU Operator Helm chart. Even if a driver version is specified, the Operator would still deploy the driver container unless driver.enabled is set to false. Pinning a version does not disable driver management; it only selects which driver container image is used. Therefore, this approach fails to meet the requirement of avoiding driver management.
- ✗
Deploy the GPU Operator but remove the nvidia-driver-daemonset after installation.
Why it's wrong here
Removing the nvidia-driver-daemonset after installation is not a supported configuration. The GPU Operator's reconciliation loop would detect the missing daemonset and recreate it, potentially causing conflicts with the pre-installed driver. This manual intervention is fragile and not recommended. The correct approach is to disable driver management at install time via the appropriate Helm value, not to delete resources post-deployment.
About these practice questions
Courseiva writes every NCP-AIO question from scratch — 309 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.