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NVIDIA AI Infrastructure Sample Questions:

1. You are deploying a multi-node A1 training cluster using Kubernetes, with each node equipped with multiple NVIDIA GPUs. You want to ensure that the Kubernetes scheduler is aware of the GPU resources available on each node and can efficiently allocate GPU-enabled pods to the appropriate nodes. Besides installing the NVIDIA Container Toolkit, what other components are essential for enabling GPU-aware scheduling in Kubernetes?

A) The Kubernetes Horizontal Pod Autoscaler (HPA).
B) The NVIDIA Device Plugin for Kubernetes.
C) The NVIDIA Fabric Manager
D) The NVIDIA GPU Operator.
E) The Kubernetes Resource Quota controller.

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

3. You are observing that the memory bandwidth being achieved by your CUDA application on an NVIDIAAIOO GPU is significantly lower than the theoretical peak bandwidth. Which of the following could be potential causes for this, and what actions can you take to validate or mitigate them? (Select all that apply)

A) The application is using a small transfer size per kernel launch. Increase the amount of data processed per kernel launch to amortize the overhead of kernel launch and data transfer.
B) The application is using uncoalesced memory access patterns. Refactor the code to ensure contiguous memory access by threads within a warp.
C) The system memory is fully occupied. Deallocate some memory.
D) The application is using single precision floating-point operations. Switch to double precision to increase memory bandwidth utilization.
E) The GPU is being limited by power capping. Increase the power limit using 'nvidia-smi -pl' (if permitted) to allow the GPU to operate at higher clock speeds.

4. Which of the following statements are correct regarding the use of NVIDIA GPUs with Docker containers?

A) CUDA libraries are required inside the container if your application uses CODA.
B) Using environment variables like 'CUDA VISIBLE DEVICES' within the container can influence which GPUs are accessible to the application.
C) The NVIDIA Container Toolkit allows you to run GPU-accelerated applications in Docker containers without modifying the container image.
D) You must install NVIDIA drivers inside the Docker container to enable GPU support.
E) The 'nvidia-smr command can only be run on the host machine, not inside a Docker container.

5. After installing a new NVIDIA GPU in an AI server, you run 'nvidia-smi' and receive the error 'NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.' You have verified the driver is installed. What is the MOST likely cause?

A) The system BIOS needs to be updated.
B) The power supply is insufficient to power the GPU.
C) The GPU is not properly seated in the PCIe slot.
D) The secure boot is enabled preventing the driver from initializing.
E) The wrong driver version is installed for the installed CUDA version.

Solutions: