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NVIDIA AI Infrastructure Sample Questions:

1. Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. Which strategy or tool would be least effective in mitigating these memory allocation stalls?

A) Enabling CUDA graph capture to reduce kernel launch overhead.
B) Increasing the GPU's TCC (Tesla Compute Cluster) mode priority.
C) Optimize the model using TensorRT.
D) Reducing the model's memory footprint by using quantization or pruning techniques.
E) Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests.

2. A data center is designed for A1 training with a high degree of east-west traffic. Considering cost and performance, which network topology is generally the most suitable?

A) Bus
B) Mesh
C) Ring
D) Spine-Leaf
E) Three-Tier

3. An AI server with 8 GPUs is experiencing random system crashes under heavy load. The system logs indicate potential memory errors, but standard memory tests (memtest86+) pass without any failures. The GPUs are passively cooled. What are the THREE most likely root causes of these crashes?

A) A faulty power supply unit (PSU) that is unable to provide stable power under peak load.
B) Network congestion causing intermittent data corruption during distributed training.
C) GPIJ memory errors that are not detectable by standard CPU-based memory tests.
D) Incompatible NVIDIA driver version with the installed Linux kernel.
E) Insufficient airflow within the server, leading to overheating of the GPUs and VRMs.

4. You are developing a distributed deep learning application that uses multiple GPUs across several Docker containers running on different physical servers. How do you ensure that each container can access and utilize the GPUs on its respective host?

A) Manually configure each container to use the 'CUDA VISIBLE DEVICES' environment variable to specify the GPUs it should use on its respective host.
B) Use Docker Swarm and specify GPU resource constraints in the 'docker-compose.yml' file to allocate GPUs to each service.
C) Install the same version of NVIDIA drivers on all host machines and configure a network file system (NFS) to share the CUDA libraries between the containers.
D) Ensure the NVIDIA Container Toolkit is installed and configured on each host machine, and use a container orchestration platform like Kubernetes to manage the deployment and GPU assignment.
E) Create a custom Docker network and configure each container to use the network's gateway as the default GPU device.

5. Consider the following scenario: You have a BlueField-2 DPU installed in a server. You are trying to establish RDMA (Remote Direct Memory Access) communication between the DPU and another server. However, the RDMA connection fails. Which of the following is the most crucial factor to verify in this scenario?

A) That the power supply to the DPU is providing sufficient wattage.
B) That the clocks on both servers are synchronized using NTP (Network Time Protocol).
C) That the correct MTU (Maximum Transmission Unit) is configured on the network interfaces involved in the RDMA connection.
D) That the TCP window size is properly tuned for high-bandwidth communication.
E) That the appropriate RDMA kernel modules (e.g., , 'ib_uverbs) are loaded on both the DPUand the remote server.

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