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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 2	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 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 (Q124-Q129):

NEW QUESTION # 124
A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. 'nvidia-smi' reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others. What is the MOST likely cause and the immediate action to take?

A. The temperature sensors on GPUs 3 and 4 are faulty; replace the GPUs immediately.
B. A driver issue is causing incorrect temperature reporting; reinstall the NVIDIA driver.
C. There is a localized airflow problem affecting GPUs 3 and 4; check fan speeds and airflow obstructions.
D. The power supply is failing to provide sufficient power to GPUs 3 and 4; replace the power supply.
E. A software bug in the CUDA toolkit is causing the errors; downgrade to an earlier version.

Answer: C

Explanation:
Elevated inlet temperatures, despite normal GPU temperatures, strongly suggest an airflow issue. GPUs 3 and 4 are likely positioned in a way that restricts airflow. The first step is to check fan speeds and for any physical obstructions blocking airflow. Replacing components without addressing the airflow issue will not solve the problem.

NEW QUESTION # 125
An AI infrastructure uses a combination of air-cooled and liquid-cooled NVIDIA GPUs. You want to optimize cooling performance based on the specific thermal characteristics of each GPU type and their location within the server rack. How can you achieve granular cooling control and monitoring to address these heterogeneous cooling requirements effectively? SELECT TWO.

A. Deploy per-server cooling solutions with independent fan control for each server node, allowing for tailored airflow adjustments.
B. Use a centralized monitoring system to track GPU temperatures and power consumption, but apply the same cooling profile to all GPUs regardless of type.
C. Implement dynamic fan speed control based on individual GPU temperatures, leveraging tools like 'nvidia-smi' and custom scripts, for air-cooled GPUs.
D. Employ liquid cooling only for the highest TDP GPUs and rely on ambient air cooling for all other components.
E. Implement rack-level airflow management solutions, such as blanking panels and cable management, to improve overall airflow uniformity.

Answer: C,E

Explanation:
Implementing rack-level airflow management (A) improves overall airflow uniformity, which benefits all GPUs, regardless of cooling type. Implementing dynamic fan speed control based on individual GPU temperatures for air-cooled GPUs (E) allows for fine-grained adjustments to cooling performance. Per-server cooling solutions (C) can be helpful, but less scalable/practical in most datacenters. Using the same cooling profile for all GPUs (B) is ineffective. Cooling only high TDP GPUs (D) may not be sufficient.

NEW QUESTION # 126
After installing NGC CLI on RHEL, a user runs ngc registry image list but sees no results. The API key and org are correctly configured. What resolves this?

A. Disable SELinux to eliminate unnecessary security restrictions.
B. Ensure the user's NGC account has 'REGISTRY_READ' permissions for the org.
C. Reinstall the CLI using the yum command instead of manual installation.
D. Run ngc config set --team team-name to specify a team.

Answer: B

Explanation:
If the API key and organization are configured correctly but registry image listings return no results, the account likely lacks the required registry access within that organization. Granting REGISTRY_READ permission allows the NGC CLI to view and list registry images.

NEW QUESTION # 127
You are replacing a faulty NVIDIA Tesla V 100 GPU in a server. After physically installing the new GPU, the system fails to recognize it. You've verified the power connections and seating of the card. Which of the following steps should you take next to troubleshoot the issue?

A. Reinstall the operating system to ensure proper driver installation.
B. Disable and re-enable the GPU slot in the system BIOS.
C. Check if the new GPU requires a different driver version than the currently installed one and update if needed.
D. Immediately RMA the new GPU as it is likely defective.
E. Update the system BIOS and BMC firmware to the latest versions.

Answer: C,E

Explanation:
After verifying the physical installation, the next steps are to ensure the system's firmware is up-to-date and that the correct drivers are installed. Older BIOS/BMC firmware may not properly recognize newer GPUs, and incorrect drivers will prevent the GPU from functioning correctly. RMAing the new GPU or reinstalling the OS prematurely are inefficient troubleshooting steps. The system BIOS may have an option to disable and enable the GPU slot, but that would be rare.

NEW QUESTION # 128
An AI server equipped with multiple NVIDIA GPUs experiences frequent reboots during peak workload periods. The system event logs indicate 'Uncorrectable Machine Check Exception' errors. You suspect a power delivery issue. Besides checking the PSUs, what other hardware component(s) should be thoroughly inspected to identify potential causes?

A. The network interface cards (NICs).
B. The server's CMOS battery.
C. The CPU and system memory.
D. The storage drives (SSDs/HDDs).
E. The motherboard VRMs (Voltage Regulator Modules) responsible for supplying power to the GPUs.

Answer: E

Explanation:
While 'Uncorrectable Machine Check Exception' errors can have various causes, a power delivery issue to the GPUs is a strong possibility in this scenario. The motherboard VRMs are responsible for regulating and supplying power to the GPUs. If they are failing or inadequate, it can lead to power instability and these types of errors during high load.

NEW QUESTION # 129
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