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NVIDIA AI Infrastructure Sample Questions:

1. An NVIDIA DGX server with 8 GPUs is experiencing performance issues during a distributed deep learning training run. You suspect a problem with the GPU interconnects. You have already confirmed that NVLink is active. What is the most thorough approach to diagnose potential bandwidth or latency bottlenecks in the GPU-to-GPlJ communication paths?

A) Use 'nvidia-smi topo -m' to visualize the GPU topology and check the reported link speeds. Any links with significantly lower speeds are suspect.
B) Run NCCL all-reduce benchmarks (e.g., using the NCCL tests) to measure the actual communication bandwidth between all pairs of GPUs. Compare the results to expected theoretical peak bandwidth.
C) All of the above
D) Examine the output of 'dmesg' for any NVLink-related error messages or warnings.
E) Monitor GPU utilization with 'nvidia-smi' during the training run. Uneven utilization across GPUs indicates a potential communication bottleneck.

2. After replacing a GPU in a multi-GPU server, you notice that the new GPU is consistently running at a lower clock speed than the other GPUs, even under load. *nvidia-smi' shows the 'Pwr' state as 'P8' for the new GPU, while the others are at 'PO'. What is the MOST probable cause?

A) The new GPU is not receiving sufficient power; check the power connections and PSU capacity.
B) The new GPU requires a firmware update that hasn't been applied.
C) The new GPU is overheating and throttling performance.
D) The new GPU is a lower-performance model than the other GPUs.
E) The driver is not properly recognizing the new GPU's capabilities; reinstall the driver.

3. A distributed training job using multiple nodes, each with eight NVIDIA GPUs, experiences significant performance degradation. You notice that the network bandwidth between nodes is consistently near its maximum capacity. However, 'nvidia-smi' shows low GPU utilization on some nodes. What is the MOST likely cause?

A) Data is not being distributed evenly across the nodes; some nodes are waiting for data from others.
B) The GPUs are overheating, causing thermal throttling.
C) The NVIDIA drivers are outdated, causing communication bottlenecks.
D) The network interface cards (NICs) are faulty, causing packet loss and retransmissions.
E) The CPU is heavily loaded, causing contention for network resources.

4. You're designing a new InfiniBand network for a distributed deep learning workload. The workload consists of a mix of large-message all- to-all communication and small-message parameter synchronization. Considering the different traffic patterns, what routing strategy would MOST effectively minimize latency and maximize bandwidth utilization across the fabric?

A) Implement a purely deterministic routing scheme, disabling all adaptive routing features.
B) Rely solely on the default Subnet Manager (SM) with a Min Hop path selection algorithm.
C) Implement a static routing scheme with manually configured forwarding tables on each switch.
D) Utilize a combination of Adaptive Routing (AR) to handle dynamic traffic patterns and Quality of Service (QOS) to prioritize small-message parameter synchronization.
E) Disable multicast.

5. You are building a Docker image for a deep learning application that requires an NVIDIA GPU. Which of the following instructions is the most efficient way to ensure the NVIDIA drivers are available within the container, assuming you are using the nvidia/cuda/' base image and want to minimize the image size?

A) FROM nvidia/cuda:ll .4.2-base-ubuntu20.04 AS builder RUN apt-get update && apt-get install -y -no-install-recommends software-properties-common RUN add-apt-repository ppa:graphics-drivers/ppa RUN apt-get update && apt-get install -y -no-install-recommends nvidia-driver-470 FROM ubuntu:20.04 COPY -from=builder /usr/lib/nvidia /usr/lib/ COPY -from=builder /usr/local/nvidia /usr/local/
B) Using 'nvidia/cuda' base image, the drivers are already included, so no further action is needed.
C) FROM nvidia/cuda:ll .4.2-base-ubuntu20.04 RUN apt-get update && apt-get install -y -no-install-recommends nvidia-driver-470
D) COPY /usr/lib/nvidia /usr/local/nvidia/
E) RUN apt-get update && apt-get install -y nvidia-driver-470

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