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NVIDIA AI Infrastructure Sample Questions:

1. Consider an AI server equipped with two NVIDIAAI 00 GPUs interconnected with NVLink. You want to maximize the memory bandwidth available to a CUDA application. You observe that the application's performance doesn't scale linearly with the number of GPUs. Which of the following coding techniques or configurations could potentially improve inter-GPU memory access performance?

A) Disable NVLink to force the application to use PCle, which might provide more consistent performance.
B) Employ Unified Memory (I-JM) with prefetching to automatically migrate data between GPUs as needed.
C) Use CUDA-aware MPl for inter-GPU communication to leverage NVLink.
D) Manually manage data transfers between GPUs using 'cudaMemcpyPeer' to exploit NVLink bandwidth. Choose the GPU with more free memory for allocations.
E) Ensure all memory allocations are performed on GPU O to minimize data transfer.

2. You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. 'nvidia-smi' reports a persistent "XID 13" error for that GPU. What is the most likely cause?

A) An incorrect CUDA version installed.
B) Insufficient system memory preventing data transfer to that GPU.
C) The GPU's compute mode is set to 'Exclusive Process'.
D) A driver bug causing incorrect workload distribution.
E) A hardware fault within the GPU, such as a memory error or core failure.

3. Which of the following is the MOST critical consideration when planning the cooling strategy for a server rack containing multiple NVIDIA A100 GPUs?

A) Optimizing airflow to ensure hot air is efficiently exhausted from the rack and cool air is drawn in.
B) Increasing the fan speed of the server chassis fans to maximum.
C) Applying thermal paste to the GPU memory chips.
D) Ensuring the server room temperature is kept below 25 degrees Celsius.
E) Using liquid cooling for the CPUs, but air cooling for the GPUs.

4. A user reports that their CUDA application is running slower than expected after an NVIDIA driver update. You suspect a driver compatibility issue. How can you revert to a previous NVIDIA driver version on an Ubuntu system, assuming you have the older driver package 'nvidia-driver-470 470.82.00-0ubuntu1_amd64.deb'?

A) First, remove the current NVIDIA driver using 'sudo apt purge nvidia- s, then install the .deb' package using 'sudo dpkg -i nvidia-driver-470_470.82.00-Oubuntu1_amd64.deb' and resolve any dependency issues with 'sudo apt --fix-broken install'.
B) Install the older .run' installer version, if you previously installed it using .run', using the -uninstall option first.
C) Simply install the .deb' package using 'sudo dpkg -i
D) Edit the '/etc/apt/sources.list' file to point to an older repository containing the desired driver version, then update and upgrade the system.
E) Use the NVIDIA System Management Interface Cnvidia-smi') to downgrade the driver.

5. Your AI training pipeline involves a pre-processing step that reads data from a large HDF5 file. You notice significant delays during this step. You suspect the HDF5 file structure might be contributing to the slow read times. What optimization technique is MOST likely to improve read performance from this HDF5 file?

A) Encrypting the HDF5 file for enhanced security.
B) Storing the HDF5 file on a network file system like NFS.
C) Reorganizing the HDF5 file to improve data contiguity and chunking.
D) Compressing the HDF5 file using gzip.
E) Converting the HDF5 file to a CSV file.

Solutions: