100% Pass Quiz NVIDIA - NCP-AII - NVIDIA AI Infrastructure–Efficient Exam Simulations

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NVIDIA NCP-AII Exam Overview:

Certification Vendor:	NVIDIA
Exam Name:	NVIDIA Certified Professional – AI Infrastructure
Exam Number:	NCP-AII
Exam Format:	Multiple Choice, Multiple Select
Certificate Validity Period:	2 years
Exam Price:	$195 USD
Related Certifications:	NCP-DES NCP-AI
Available Languages:	English
Real Exam Qty:	50
Exam Duration:	90 minutes
Passing Score:	700 (scale of 0-1000)
Sample Questions:	NVIDIA NCP-AII Sample Questions
Exam Way:	Online proctored exam (Pearson VUE)
Pre Condition:	Recommended: hands-on experience with NVIDIA AI infrastructure products; basic knowledge of Linux, networking, and data center operations
Official Syllabus URL:	https://www.nvidia.com/en-us/certifications/ncp-ai-infra/

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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
Topic 2	System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC OOB TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Topic 3	Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm Enroot Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.
Topic 4	Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD Intel servers and storage.
Topic 5	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.

NVIDIA AI Infrastructure Sample Questions (Q118-Q123):

NEW QUESTION # 118
The system administrator plans to use Multi-instance GPU profiles. What command should be used to verify that the GPU has this mode enabled?

A. nvidia-enable
B. nvidia-mig
C. nvidia-smi
D. nvidia-mode

Answer: C

Explanation:
nvidia-smi is the standard NVIDIA management command used to verify whether MIG mode is enabled on a supported GPU. It displays the GPU's MIG status and can also show configured MIG instances and profiles.

NEW QUESTION # 119
You have configured MIG on your A100 GPU, creating several MIG instances. You now want to allocate a specific MIG instance to a Docker container. How would you specify the necessary device option when running the 'docker run' command to ensure the container uses only that MIG instance? Assuming the MIG instance UUID is GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

A. docker run -device=/dev/nvidia0 .
B. docker run -gpus device=GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ...
C. docker run -gpus all .
D. docker run -runtime=nvidia --device=GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ...
E. docker run -nvidia-visible-devices GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

Answer: B

Explanation:
The correct way to specify a specific MIG instance for a Docker container is using the '-gpus device=' option with the MIG instance UUID. This ensures the container only has access to the designated MIG instance. '-gpus all' grants access to all GPUs. "-device=/dev/nvidiaC provides access to the entire GPU. '--runtime=nvidia' is required for NVIDIA GPU support but doesn't specify a particular instance. There is no option unvidia-visible-devices'.

NEW QUESTION # 120
A system administrator needs to install the NVIDIA Container Toolkit and perform the following actions:
- Update apt
- Issue the install command
- Configure the Docker daemon to recognize the NVIDIA Container Runtime
- Restart the Docker daemon to complete the installation.
What step should be taken first?

A. Initialize NVIDIA Omniverse configuration
B. Install SNAP package manager
C. Add NVIDIA Repository
D. Stop toolkit process

Answer: C

Explanation:
Before running apt update and installing the NVIDIA Container Toolkit package, the NVIDIA package repository must be added so the package manager can locate and install the correct toolkit packages.

NEW QUESTION # 121
Consider the following 'Ispci' output snippet after installing an NVIDIA GPU:
03:00.0 VGA compatible controller: NVIDIA Corporation Device 2236 (rev al) Subsystem: Dell Device 1234 Kernel driver in use: nvidia Kernel modules: nvidiafb, nouveau, nvidia drm, nvidia What does this output indicate, and what is the POTENTIAL issue?

A. The GPU is installed, but the 'nvidiafb' driver is causing conflicts. Solution: Remove the 'nvidiafb' module.
B. The output indicates a hardware failure and requires a replacement of the GPU.
C. Solution: Blacklist 'nouveau' and ensure the NVIDIA driver is correctly configured.
D. The GPU is installed, but the open-source 'nouveau' driver is loaded instead of the proprietary NVIDIA driver, which could lead to suboptimal performance.
E. The GPU is not properly installed, as indicated by 'Device 2236'. Solution: Re-seat the GPU.
F. The GPU is correctly installed, and the proprietary NVIDIA driver is loaded.

Answer: D

Explanation:
The output shows that both the 'nvidia' driver and the 'nouveau' driver are loaded. 'nouveau' is an open-source driver, and while it allows the GPU to function, it does not provide the performance benefits of the proprietary NVIDIA driver. The presence of 'nouveau' can sometimes interfere with the proper loading of the NVIDIA driver. Blacklisting 'nouveau' ensures that only the NVIDIA driver is used.

NEW QUESTION # 122
After Spectrum-X fabric deployment, NCCL tests show intermittent latency spikes. Which network condition most severely impacts East-West bandwidth?

A. Multiple transceiver firmware mismatches.
B. 400G port utilization at 70% on several nodes during tests.
C. Packet loss >0.001% causing NCCL pipeline stalls.
D. Jitter below 5us with consistent latency.

Answer: C

Explanation:
Packet loss is especially damaging to East-West AI fabric performance because NCCL collective operations depend on reliable, high-throughput GPU-to-GPU communication. Even small packet- loss rates can trigger retries and pipeline stalls, causing latency spikes and reducing effective bandwidth across the fabric.

NEW QUESTION # 123
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