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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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3	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 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	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.

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NVIDIA AI Infrastructure Sample Questions (Q82-Q87):

NEW QUESTION # 82
You are tasked with configuring an NVIDIA NVLink Switch system. After physically connecting the GPUs and the switch, what is the typical first step in the software configuration process?

A. Updating the firmware of the NVLink Switch.
B. Installing the latest NVIDIA drivers on all connected GPUs.
C. Running a memory bandwidth test between all connected GPUs.
D. Installing the NVLink Switch management software.
E. Configuring the system BIOS to enable NVLink support.

Answer: A

Explanation:
Updating the NVLink Switch firmware is crucial for ensuring compatibility and stability with the connected GPIJs and the overall system. It addresses potential bugs, security vulnerabilities, and performance issues. It should always be done first before any other software configuration. BIOS settings should be checked beforehand, and the NVLink management software comes after the firmware update.

NEW QUESTION # 83
You are installing four NVIDIAAIOO GPUs in a server, and after installation, you observe that the PCle link speed for one of the GPUs is running at x8 instead of the expected x16. What could be the POSSIBLE causes for this reduced PCle link speed?

A. The BIOS/UEFI is configured to limit the PCle link speed for that slot.
B. The CPU does not have enough PCle lanes to support all GPUs at x16.
C. All of the above
D. The PCle slot is only wired for x8 speed.
E. The GPU is faulty.

Answer: C

Explanation:
A reduced PCle link speed can result from multiple factors: a faulty GPU, insufficient PCle lanes from the CPU, the physical wiring of the PCle slot, or a BIOS/IJEFI configuration limiting the speed. All these are potentially viable cause. Thus answer E is correct.

NEW QUESTION # 84
You've deployed a GPU-accelerated application in Kubernetes using the NVIDIA device plugin. However, your pods are failing to start with an error indicating that they cannot find the NVIDIA libraries. Which of the following could be potential causes of this issue? (Multiple Answers)

A. The NVIDIA device plugin is not properly configured in the Kubernetes cluster.
B. The application container image does not include the necessary NVIDIA libraries.
C. The GPU's compute capability is not sufficient for the workload.
D. The NVIDIA drivers are not installed on the host node.
E. The 'nvidia-container-runtime' is not configured as the default runtime for Docker/containerd.

Answer: A,B,D,E

Explanation:
If pods cannot find NVIDIA libraries, it could be because the drivers are missing on the host, the container runtime is not configured to use the NVIDIA runtime, the NVIDIA device plugin is misconfigured preventing GPU discovery and allocation, or the application container image does not include the NVIDIA libraries. E is likely incorrect, if the GPU's compute capability is insufficient then the app would likely start, but throw an error when trying to use the GPU.

NEW QUESTION # 85
A GPU in your AI server consistently overheats during inference workloads. You've ruled out inadequate cooling and software bugs.
Running 'nvidia-smi' shows high power draw even when idle. Which of the following hardware issues are the most likely causes?

A. Insufficient system RAM.
B. Degraded thermal paste between the GPU die and the heatsink.
C. Incorrectly seated GPU in the PCle slot, leading to poor power delivery.
D. A failing voltage regulator module (VRM) on the GPU board, causing excessive power leakage.
E. A BIOS setting that is overvolting the GPU.

Answer: B,C,D

Explanation:
Degraded thermal paste loses its ability to conduct heat effectively. A failing VRM can cause excessive power draw and heat generation. An incorrectly seated GPU can cause instability and poor power delivery, leading to overheating. Overvolting in BIOS will definitely cause overheating. While insufficient RAM can cause performance issues, it is less likely to lead to overheating.

NEW QUESTION # 86
Which command is used to enable MIG mode on an NVIDIAAmpere architecture GPU?

A. nvidia-smi -i 0 -mig 1
B. nvidia-smi -i 0 -enable-mig
C. nvidia-smi -format=csv,noheader=l -i 0 -enable-mig
D. nvidia-smi -set-mig-mode=enable -i 0
E. nvidia-smi -gpu 0 -i MIG=I

Answer: D

Explanation:
The correct command to enable MIG mode is 'nvidia-smi -set-mig-mode=enable -i , where '-i 0' specifies the GPU index. This command instructs the driver to reconfigure the GPU to enable MIG functionality. The other commands either use incorrect syntax or lack the necessary functionality for enabling MIG mode.

NEW QUESTION # 87
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