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NVIDIA AI Infrastructure Sample Questions:

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

2. You want to limit the GPU memory available to a specific Docker container running a deep learning model. Which of the following 'docker run' commands using the NVIDIA Container Toolkit is the most appropriate?

A) docker run -gpus 'device=0:memory=4g" my-image
B) docker run -gpus all -memory 4g my-image
C) docker run -gpus 'device=GPU-UUlD:memory=4g" my-image
D) docker run -gpus device=0 --memory 4g my-image
E) docker run -gpus all,memory=4g my-image

3. You are running a large-scale distributed training job on a cluster of AMD EPYC servers, each equipped with multiple NVIDIAA100 GPUs. You are using Slurm for job scheduling. The training process often fails with NCCL errors related to network connectivity. What steps can you take to improve the reliability of the network communication for NCCL in this environment? Choose the MOST appropriate answers.

A) Ensure that the InfiniBand or RoCE network is properly configured and that all servers can communicate with each other over the network. Verify the network interface names and IP addresses in the NCCL configuration.
B) Disable the firewall on all servers to allow unrestricted network communication.
C) Use the Slurm 'srun' command with the '-mpi=pmi2 option to launch the training job. This ensures that Slurm properly initializes the MPl environment and sets the NCCL environment variables.
D) Increase the 'NCCL CONNECT TIMEOUT and *NCCL TIMEOUT environment variables to allow for longer network delays.
E) Decrease the batch size to reduce the amount of data transferred over the network.

4. You're setting up a BlueField-3 DPIJ to offload storage virtualization tasks. Specifically, you want to use SPDK (Storage Performance Development Kit) on the DPIJ. What are the MINIMUM required steps to enable SPDK on the BlueField-3 after the DPIJ has been flashed with the appropriate OS image? (Select TWO)

A) Download and compile the SPDK source code directly on the DPU.
B) Install the SPDK packages using the DPU's package manager (e.g., 'apt install spdk').
C) Enable the SPDK service using 'systemctl enable spdK and 'systemctl start spdk'.
D) Configure the network interfaces on the DPIJ to support RDMA or NVMe-oF, depending on the desired storage protocol.
E) Configure the Huge Pages settings in the DPU's kernel to allocate sufficient memory for SPDK.

5. Which of the following are key considerations when choosing between CPU pinning and NUMA (Non-Uniform Memory Access) awareness for a distributed training job on a multi-socket AMD EPYC server with multiple GPUs?

A) Both CPU pinning and NUMA awareness are critical for optimizing performance. They should be used in conjunction to achieve optimal performance.
B) CPU pinning ensures that each process/thread runs on a specific CPU core, reducing context switching overhead. NUMA awareness ensures that the CPU cores and memory used by a process are located within the same NUMA node, minimizing memory access latency.
C) Neither CPU pinning nor NUMA awareness are relevant for GPIJ-accelerated workloads, as the GPUs handle all the computation.
D) NUMA awareness is generally more important than CPU pinning because it directly impacts memory bandwidth.
E) CPU pinning is generally more important than NIJMA awareness because it directly impacts CPU utilization.

Solutions: