NCP-AIO Installation and Deployment Practice Question
When deploying NVIDIA AI Enterprise, why is the selection of the correct CUDA version in the container image critical during the installation phase?
⚠ Common exam trap
Candidates often assume that the container image includes its own driver, failing to realize that the host driver must be compatible with the CUDA version installed inside 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
✓
The CUDA version must be compatible with the host driver version.
The CUDA version dictates which APIs and features are available to the AI application. Because the driver on the host must support the CUDA version used by the container (backward compatibility), mismatching these leads to runtime failures. This is a crucial AI Ops consideration as it directly affects the stability of the entire stack, ensuring that the software environment aligns with the underlying hardware capabilities for maximum performance and reliability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It determines the speed of the GPU's memory bus.
Why it's wrong here
Memory bus speed is a fixed hardware characteristic determined by the GPU architecture and clock settings. The CUDA version is a software-defined interface for programming the GPU and has absolutely no impact on the physical electrical signaling or frequency of the hardware's memory bus interfaces.
- ✓
The CUDA version must be compatible with the host driver version.
Why this is correct
NVIDIA drivers follow a backward-compatibility model where the driver must support the CUDA version used by the application. Using a container with a newer CUDA version than the driver supports will cause the application to fail to initialize, as it cannot properly map the required kernel functions.
- ✗
It enables the use of the NVIDIA License System.
Why it's wrong here
License management is handled by the NLS client libraries and the licensing service, independent of the CUDA version. While some features might require specific software versions, the actual mechanism of license validation is decoupled from the CUDA programming environment and its internal library versions.
- ✗
It is required for the installation of the GPU Operator.
Why it's wrong here
The GPU Operator is an infrastructure-level management tool that operates independently of the application's specific CUDA environment. It manages the underlying drivers and runtimes and does not require a specific CUDA version to be present within the application images in order to function correctly.
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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.