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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 5	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.

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NVIDIA AI Infrastructure Sample Questions (Q21-Q26):

NEW QUESTION # 21
An AI inference workload running on a server equipped with NVIDIA TensorRT is experiencing intermittent performance degradation. Profiling reveals potential issues with GPU utilization. Which command-line tool is best suited to provide real-time GPU performance metrics, including utilization, memory usage, and power consumption, during workload execution?

A. nvprof
B. gpustat
C. nvidia-smi
D. tegrastats
E. nvitop

Answer: C

Explanation:
'nvidia-smi' (NVIDIA System Management Interface) is the primary tool for monitoring GPU performance in real-time, including utilization, memory usage, and power consumption. While 'nvprof is a profiler, it's more suited for in-depth analysis after the run. 'nvitop' and gpustat are third-party tools that provide similar functionalities as -nvidia-smr. 'tegrastats' is specific to NVIDIA Jetson devices.

NEW QUESTION # 22
You are installing four NVIDIAAIOO GPUs into a server designed for AI training. The server motherboard has multiple PCIe Gen4 x16 slots. However, the server's power supply unit (PSU) only has three 8-pin PCIe power connectors available. What is the BEST course of action to ensure all GPUs receive adequate power?

A. Underclock the GPUs significantly to reduce their power consumption below the available PSU capacity.
B. Use a PCIe power splitter cable on one of the 8-pin connectors to power two GPUs.
C. Connect the GPUs using the motherboard's internal SATA power connectors.
D. Replace the existing PSU with a higher wattage PSU that has at least four 8-pin PCIe power connectors.
E. Install only three GPUs and leave the fourth unpowered.

Answer: D

Explanation:
Using a splitter can overload the original connector and cause instability or damage. Leaving a GPU unpowered defeats the purpose. SATA connectors are not designed for the high power requirements of GPUs. Underclocking might work, but is not a reliable long-term solution. Replacing the PSU is the safest and most reliable solution.

NEW QUESTION # 23
Which of the following statements are correct regarding the use of NVIDIA GPUs with Docker containers?

A. The NVIDIA Container Toolkit allows you to run GPU-accelerated applications in Docker containers without modifying the container image.
B. Using environment variables like 'CUDA VISIBLE DEVICES' within the container can influence which GPUs are accessible to the application.
C. CUDA libraries are required inside the container if your application uses CODA.
D. The 'nvidia-smr command can only be run on the host machine, not inside a Docker container.
E. You must install NVIDIA drivers inside the Docker container to enable GPU support.

Answer: A,B,C

Explanation:
The NVIDIA Container Toolkit allows GPU-accelerated apps to run in Docker without altering the image. The host's drivers are leveraged. CUDA libraries are necessary inside the container if your app uses CUDA. is used to control GPU visibility within the container. Drivers are not needed inside the container because they're managed by the host (making B incorrect), and 'nvidia-smi' can be run inside containers if the NVIDIA Container Toolkit is properly set up (making C incorrect).

NEW QUESTION # 24
A DGX server reports degraded performance and storage alerts. How would you use NVSM (NVIDIA System Management) and nvidia-smi to troubleshoot both system and GPU issues?

A. Use nvsm show health for a system health summary, nvsm show storage for storage issues, and nvidia-smi -q to get detailed GPU information.
B. Run nvsm collect-stats to gather logs, use lsblk to understand if there are storage problems, and nvidia-smi -q to get detailed GPU information.
C. Start by issuing nvidia-smi -L to list GPUs, followed by nvsm --refresh to clear all alerts, and nvidia-smi -q to get detailed GPU information.
D. Run nvsm reset to restore system health, then use nvidia-smi --fix for automatic GPU repairs and status recovery.

Answer: A

Explanation:
nvsm show health provides the overall DGX system health summary, while nvsm show storage focuses on storage-related alerts and status. nvidia-smi -q provides detailed GPU diagnostics such as temperature, power, clocks, ECC, utilization, and error information, making this the best combined approach for troubleshooting both storage/system and GPU performance issues.

NEW QUESTION # 25
After a recent OS upgrade, you need to reinstall NVIDIA GPU and DOCA drivers to support both AI training and accelerated networking. What best practice ensures successful installation and full hardware capability?

A. Apply legacy drivers for hardware released within the last two years to maintain maximum compatibility across versions.
B. Download and install only the specific versions of GPU and DOCA drivers listed as compatible with the current OS and hardware.
C. Install the latest available drivers directly from the NVIDIA website.
D. Use the default drivers provided by the Linux distribution, unless an installation fails during system boot.

Answer: B

Explanation:
The correct best practice is to install only the GPU and DOCA driver versions that are validated for the current operating system, kernel, hardware platform, and NVIDIA software release. In NVIDIA AI infrastructure, driver compatibility is critical because GPU drivers, CUDA libraries, DCGM, Fabric Manager, DOCA-OFED, network adapters, DPUs, and kernel modules must align. Installing the newest driver without checking compatibility can cause driver/library mismatches, failed nvidia-smi, broken RDMA, or unsupported DOCA behavior. NVIDIA DGX documentation notes that DGX OS updates include coordinated OS, kernel, GPU driver, CUDA toolkit, and DCGM updates, and NVIDIA AI Enterprise provides support matrices to confirm supported platform combinations. DOCA-OFED is also tied to the operating system and kernel environment, so version selection must be deliberate. Legacy drivers may lack required fixes or hardware support, and default distribution drivers are usually not sufficient for validated DGX or AI Enterprise deployments. After installation, administrators should reboot if required, validate GPU visibility, confirm networking driver status, and run health checks before returning the node to production.

NEW QUESTION # 26
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