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NVIDIA AI Infrastructure Sample Questions (Q161-Q166):

NEW QUESTION # 161
You are managing a server farm of GPU servers used for A1 model training. You observe frequent GPU failures across different servers.
Analysis reveals that the failures often occur during periods of peak ambient temperature in the data center. You can't immediately improve the data center cooling. What are TWO proactive measures you can implement to mitigate these failures without significantly impacting training performance?

A. Reduce the GPU power limit using 'nvidia-smi' to decrease heat generation.
B. Increase the fan speeds of the GPU coolers to improve heat dissipation.
C. Implement a more aggressive GPU frequency scaling profile to throttle performance during peak temperatures.
D. Replace all existing GPUs with water-cooled models.
E. Schedule training jobs to run during off-peak hours when ambient temperatures are lower.

Answer: A,E

Explanation:
Reducing the GPIJ power limit (A) will directly reduce heat generation, which is the primary driver of failures in this scenario. While it will slightly reduce performance, the impact is less than aggressive throttling. Scheduling training jobs for off-peak hours (D) completely avoids the issue of high ambient temperatures. Increasing fan speeds (B) can help, but it may not be sufficient and can increase noise. Frequency scaling (C) is a more aggressive approach that can significantly impact performance. Replacing all GPUs with water-cooled models (E) is a more expensive and complex solution that may not be immediately feasible.

NEW QUESTION # 162
You are designing an Ai server infrastructure using NVIDIA HGX AIOO modules. The server's power supply units (PSUs) are configured in a redundant (N+1) setup. The individual PSUs are rated for 3000W each, and the server contains three PSUs. If the expected peak power consumption of the HGX A100 modules and other components is 5500W, what is the safety margin (in Watts) in the power budget?

A. 500W
B. 0W
C. 3500W
D. 5000W
E. 9000W

Answer: A

Explanation:
With N+1 redundancy and three 3000W PSUs, the total available power is 2 3000W = 6000W (N+1 means the system can tolerate one PSU failure). The safety margin is 6000W - 5500W = 500W.

NEW QUESTION # 163
An A1 server exhibits frequent kernel panics under heavy GPU load. 'dmesg' reveals the following error: 'NVRM: Xid (PCl:0000:3B:00): 79, pid=..., name=..., GPU has fallen off the bus.' Which of the following is the least likely cause of this issue?

A. A driver bug in the NVIDIA drivers, leading to GPU instability.
B. Overclocking the GPU beyond its stable limits.
C. Insufficient power supply to the GPIJ, causing it to become unstable under load.
D. A faulty CPU.
E. A loose or damaged PCle riser cable connecting the GPU to the motherboard.

Answer: D

Explanation:
The error message GPU has fallen off the bus strongly suggests a hardware-related issue with the GPU's connection to the motherboard or its power supply. Insufficient power, a loose riser cable, driver bugs and overclocking can all lead to this. A faulty CPU, while capable of causing system instability, is less directly related to the GPIJ falling off the bus and therefore the least likely cause in this specific scenario.

NEW QUESTION # 164
When deploying a BlueField DPIJ in an inline security appliance role, which networking configuration is MOST efficient and provides the lowest latency for inspecting all traffic passing through the server?

A. Configure the BlueField as a router, performing Layer 3 forwarding between subnets.
B. Utilize a switchdev-based configuration where the DPIJ directly programs the switch ASIC to steer traffic.
C. Use SR-IOV to create Virtual Functions (VFs) for both the host and the DPIJ, and then route traffic between them.
D. Configure the BlueField as a traditional bridge, forwarding traffic between two physical interfaces.
E. Implement a hairpin configuration where traffic is sent to the DPIJ and then looped back to the same physical interface after inspection.

Answer: B

Explanation:
A switchdev-based configuration offers the lowest latency because the DPIJ directly programs the switch ASIC. This allows for hardware-accelerated traffic steering and inspection without the overhead of traditional bridging or routing. Hairpinning introduces unnecessary latency, while SR-IOV and bridging add software processing overhead.

NEW QUESTION # 165
You have a large dataset stored on a BeeGFS file system. The training job is single node and uses data augmentation to generate more data on the fly. The data augmentation process is CPU-bound, but you notice that the GPU is underutilized due to the training data not being fed to the GPU fast enough. How can you reduce the load on the CPU and improve the overall training throughput?

A. Increase the number of BeeGFS metadata servers (MDSs) to improve metadata performance.
B. Move the training data to a local NVMe drive on the training node.
C. Enable data compression on the BeeGFS file system to reduce the amount of data being transferred over the network.
D. Implement asynchronous 1/0 in the data loading pipeline using a library like NVIDIA DALI to offload data processing tasks from the CPU to the GPU.
E. Decrease the batch size of the training job to reduce the amount of data being processed at each iteration.

Answer: D

Explanation:
Using NVIDIA DALI (option C) allows you to offload data augmentation and preprocessing tasks from the CPU to the GPU, freeing up CPU resources for other tasks and enabling faster data loading. Moving to a local NVMe drive (A) bypasses BeeGFS but doesn't address the CPU bottleneck. Increasing MDSs (B) improves metadata performance but doesn't directly help with the CPU-bound data augmentation. Decreasing the batch size (D) reduces the workload but doesn't solve the underlying CPU bottleneck. Data compression (E) can increase CPU load due to the decompression process.

NEW QUESTION # 166
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Lee Tate Lee Tate

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