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NVIDIA AI Infrastructure Sample Questions:

1. You have a dataset with many small files (e.g., images). Directly reading these files can result in high metadata access overhead. What are the MOST effective strategies to mitigate this problem?

A) Store the files on a file system with very small block sizes
B) Combine small files into larger archive files (e.g., using TAR or HDF5)
C) Use a key-value store (e.g., LevelDB, RocksDB) to store the file data and metadata
D) Increase the number of metadata servers in the parallel file system
E) Replicate the dataset to increase read availability

2. After installing NGC CLI using pip, you encounter 'ngc' command not found error even though pip install reported successful. What can be the cause?

A) The python executable where NGC CLI got installed is not in the system PATH.
B) NGC CLI only works inside Docker containers.
C) The shell needs to be reloaded or a new terminal session initiated for PATH changes to take effect.
D) The NGC CLI installation was corrupted. Run 'pip install -force-reinstall nvidia-cli'
E) The host's operating system is not supported by NGC CLI.

3. You are designing an AI infrastructure cluster for training large language models (LLMs). The dataset consists of 10TB of image data and 5TB of text dat a. You estimate that intermediate training data (checkpoints, temporary files) will require an additional 20TB of storage. You want to use a parallel file system for optimal performance. Considering a replication factor of 2 for data redundancy and a 20% overhead for file system metadata, what is the minimum raw storage capacity you should provision?

A) 84 TB
B) 92.4 TB
C) 42 TB
D) 100.8 TB
E) 70 TB

4. You're optimizing a deep learning model for deployment on NVIDIA Tensor Cores. The model uses a mix of FP32 and FP16 precision. During profiling with NVIDIA Nsight Systems, you observe that the Tensor Cores are underutilized. Which of the following strategies would MOST effectively improve Tensor Core utilization?

A) Enable CUDA graph capture to reduce kernel launch overhead.
B) Decrease the learning rate to improve training stability and reduce the need for gradient clipping.
C) Ensure that all matrix multiplications are performed using FP16 precision.
D) Pad the input tensors to dimensions that are multiples of 8 for optimal Tensor Core alignment.
E) Increase the batch size to fully utilize the available GPU memory.

5. You have installed an NVIDIA ConnectX-7 network adapter in an A1 server and configured RDMA over Converged Ethernet (RoCE). During validation, you observe very high latency between two servers communicating over RoCE. Which of the following are potential causes? (Choose two)

A) The network cables are damaged.
B) The network switch does not support RoCE.
C) Incorrect MTU size configuration on the network interfaces.
D) Insufficient memory on the network adapter.
E) The GPU driver is outdated.

Solutions: