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NVIDIA AI Infrastructure Sample Questions:

1. You are deploying an NVIDIA-Certified A1 server. The documentation specifies a minimum airflow requirement for the GPUs. How would you BEST monitor the GPU temperatures and ensure the airflow is adequate during a stress test?

A) Use 'nvidia-smi' to monitor GPU temperature and visually inspect the fans.
B) Use a thermal camera to measure the GPU heatsink temperature.
C) Use a software utility like 'psensor' to monitor GPU temperature.
D) Measure the ambient temperature around the server.
E) Use IPMI sensors to monitor GPU temperature and fan speeds.

2. You're managing a cluster of servers with BlueField-2 DPUs. One server is experiencing intermittent network connectivity issues. You suspect a problem with the DPU's firmware. Which of the following is the MOST reliable method to determine the CURRENT firmware version of the BlueField-2 DPIJ?

A) Examine the '/proc/driver/mlx4_core/versiorf file.
B) Run 'ethtool -i on a network interface associated with the DPIJ.
C) Query the DPIJ's BMC (Baseboard Management Controller) via IPMI or Redfish.
D) Use the 'mst status' command to query the device status and firmware version.
E) Check the system logs for firmware-related messages during boot.

3. You are configuring network fabric ports for NVIDIA GPUs in a server. The GPUs are connected to the network via PCIe. What is the primary factor that determines the maximum achievable bandwidth between the GPUs and the network?

A) The color of the Ethernet cables.
B) The clock speed of the CPU.
C) The PCIe generation and number of lanes connecting the GPUs to the network adapter (e.g., PCIe 4.0 x16).
D) The speed of the system's hard drives or SSDs.
E) The amount of system RAM.

4. You are troubleshooting a performance issue with a GPU-accelerated application running inside a Docker container. The 'nvidia-smi' output inside the container shows the GPU is being utilized, but the performance is significantly lower than expected. Which of the following could be the cause of this performance bottleneck?

A) The application is performing frequent small memory transfers between the CPU and GPIJ.
B) The GPU is overheating, causing thermal throttling.
C) The host machine's CPU is being heavily utilized, causing a bottleneck in data transfer to the GPU.
D) The version of the CUDA driver on the host is incompatible with the CUDA toolkit version used in the container.
E) The Docker container is not configured to use shared memory for data transfer with the GPU.

5. You're optimizing an Intel Xeon server with 4 NVIDIAAIOO GPUs for a computer vision application that uses CODA. You notice that the GPU utilization is fluctuating significantly, and performance is inconsistent. Using 'nvprof, you identify that there are frequent stalls in the CUDA kernels due to thread divergence. What are possible causes and solutions?

A) The CUDA driver version is incompatible with the CUDA toolkit version. Update the CUDA driver to a compatible version.
B) The CUDA compiler is generating suboptimal code. Try using different compiler optimization flags (e.g., '-O3' or '-ftz=true').
C) The input data is not properly aligned in memory. Ensure that data is aligned to 128-byte boundaries using aligned memory allocation techniques.
D) The CUDA code contains conditional branches that lead to different execution paths for different threads within the same warp. Rewrite the CUDA code to minimize branching and favor uniform execution paths within warps.
E) The GPUs are overheating, causing thermal throttling. Improve the server's cooling.

Solutions: