NCP-AIO Administration Practice Question
An administrator wants to prevent unauthorized users from accessing sensitive model weights stored in GPU memory. Which security feature should be implemented to ensure hardware-level isolation of the memory space?
⚠ Common exam trap
Candidates often confuse software-level encryption or standard disk encryption with hardware-level memory isolation. They incorrectly select general security tools instead of the specific NVIDIA Confidential Computing framework required for GPU-level memory protection.
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
✓
Implement NVIDIA Confidential Computing.
Hardware-level memory isolation via Confidential Computing technologies, such as NVIDIA Confidential Computing (CC), protects data in use by encrypting memory contents. For administrators handling sensitive IP, this provides a root-of-trust that persists even if the OS or hypervisor is compromised. This is a critical administrative control for ensuring compliance and data sovereignty in multi-tenant cloud environments where infrastructure is shared among different departments or organizations.
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 standard Linux filesystem permissions.
Why it's wrong here
Filesystem permissions only protect data at rest on disk. They do not provide any protection for data while it resides in GPU memory or during execution. An unauthorized user with access to the system can still read GPU memory contents unless specific hardware-level encryption or process isolation is configured.
- ✓
Implement NVIDIA Confidential Computing.
Why this is correct
NVIDIA Confidential Computing utilizes hardware-based Trusted Execution Environments (TEEs) to encrypt data while it resides in GPU memory. This prevents unauthorized access from other processes, the kernel, or the hypervisor, ensuring that sensitive model weights remain secure even if the software environment is considered untrusted or potentially compromised.
- ✗
Use a simple SSH firewall rule.
Why it's wrong here
Firewalls manage network traffic access to the machine. They have no visibility into the internal operation of the GPU or its memory. An authorized user who has bypassed the network layer or is already logged into the system could still access memory if isolation mechanisms are not in place.
- ✗
Update the NVIDIA CUDA shared library path.
Why it's wrong here
The library path determines where the system looks for CUDA runtime binaries. It is a configuration setting for software loading, not a security control. Modifying library paths does nothing to encrypt memory or prevent unauthorized process access to the data residing within the GPU's high-bandwidth memory (HBM) modules.
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.