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Only need to practice for 20 to 30 hours

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NVIDIA AI Infrastructure Sample Questions:

1. Which of the following is the MOST important reason for using a dedicated storage network (e.g., InfiniBand or RoCE) for AI/ML workloads compared to using the existing Ethernet network?

A) Simplified network management and configuration.
B) Reduced cost compared to upgrading the existing Ethernet infrastructure.
C) Lower latency and higher bandwidth for data transfer.
D) Automatic Quality of Service (QOS) prioritization for AI/ML traffic.
E) Improved security due to network isolation.

2. You are tasked with upgrading the NVIDIA driver on a Kubernetes node hosting GPU-accelerated A1 workloads. To minimize downtime and ensure a smooth transition, which sequence of steps should you follow?

A) Cordon the node, upgrade the driver, reboot the node, and uncordon it.
B) Upgrade the driver directly on the node, reboot the node, and let Kubernetes automatically reschedule the workloads.
C) Drain the node, upgrade the driver, reboot the node, and uncordon it.
D) Delete all pods running on the node, upgrade the driver, reboot the node, and recreate the pods.
E) Upgrade the NVIDIA container toolkit, then upgrade the driver, reboot the node, and uncordon it.

3. You are tasked with optimizing storage performance for a deep learning training job on an NVIDIA DGX server. The training data consists of millions of small image files. Which of the following storage optimization techniques would be MOST effective in reducing I/O bottlenecks?

A) Increasing the block size of the file system to the maximum supported value.
B) Enabling data compression on the storage volume.
C) Using a distributed file system with data striping across multiple storage nodes.
D) Implementing a tiered storage system with NVMe drives for frequently accessed data and HDDs for less frequently accessed data.
E) Implementing RAID 0 across all storage devices.

4. 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

5. An A1 server exhibits frequent kernel panics under heavy GPU load. 'dmesg' reveals the following error: 'NVRM: Xid (PCl:0000:3B:00): 79, pid=..., name=..., GPU has fallen off the bus.' Which of the following is the least likely cause of this issue?

A) A driver bug in the NVIDIA drivers, leading to GPU instability.
B) Insufficient power supply to the GPIJ, causing it to become unstable under load.
C) Overclocking the GPU beyond its stable limits.
D) A faulty CPU.
E) A loose or damaged PCle riser cable connecting the GPU to the motherboard.

Solutions: