NCP-AII 無料問題集「NVIDIA AI Infrastructure」

（A）An incorrect CUDA version installed.

（B）A hardware fault within the GPU, such as a memory error or core failure.

（C）A driver bug causing incorrect workload distribution.

（D）Insufficient system memory preventing data transfer to that GPU.

（E）The GPU's compute mode is set to 'Exclusive Process'.

（A）

（B）

（C）

（D）

（E）None of the above. Redfish requires specialized hardware and cannot be accessed directly via Python.

（A）One GPU's driver version is outdated, leading to inefficient power management.

（B）The server's airflow is inadequate, causing poor cooling for one of the GPUs.

（C）One GPU is defective and drawing excessive power.

（D）The ambient temperature in the server room is higher on one side of the rack.

（E）The workload is not evenly distributed between the GPUs, causing one GPU to be more heavily utilized.

（A）Examine the ambient temperature in the data center.

（B）Run a memory test on the host system.

（C）Check the CPU temperature using 'sensors' command.

（D）Analyze the system logs for GPU-related errors, specifically those indicating thermal throttling or power capping.

（E）Verify the pump speed and coolant flow rate within the liquid cooling system.

（A）A faulty CPU.

（B）A driver bug in the NVIDIA drivers, leading to GPU instability.

（C）Insufficient power supply to the GPIJ, causing it to become unstable under load.

（D）Overclocking the GPU beyond its stable limits.

（E）A loose or damaged PCle riser cable connecting the GPU to the motherboard.

（A）Download the NGC CLI package manually and install it offline.

（B）Set the 'http_proxy' and 'https_proxy' environment variables with the proxy server address and port.

（C）Use the '-proxy option with the 'pip install' command, specifying the proxy server address and port.

（D）Create a 'pip.conf file in the appropriate directory (e.g., '-/.pip/pip.conf) and configure the proxy settings within the file.

（E）NGC CLI does not support the use of proxies

（A）Ensure each GPU is installed in a slot with sufficient PCIe lanes (e.g., x16).

（B）Install the GPUs in the lowest numbered slots first.

（C）Ensure all GPUs are installed in slots of the same color.

（D）Ensure all GPUs have the same PCIe generation (e.g. Gen4).

（E）Ensure the PCIe slots are directly connected to the CPU for optimal bandwidth.

（A）The version of the CUDA driver on the host is incompatible with the CUDA toolkit version used in the container.

（B）The host machine's CPU is being heavily utilized, causing a bottleneck in data transfer to the GPU.

（C）The GPU is overheating, causing thermal throttling.

（D）The Docker container is not configured to use shared memory for data transfer with the GPU.

（E）The application is performing frequent small memory transfers between the CPU and GPIJ.

（A）nvidia-ctk runtime configure -runtime=custom -config=/etc/containerd/config.tomr

（B）nvidia-ctk runtime configure

（C）nvidia-ctk runtime install -runtime-containerd'

（D）'nvidia-docker runtime configure -runtime=containerd'

（E）nvidia-ctk runtime config -runtime=containerd -set-default'

（A）Driver incompatibility issue between NCCL and the installed NVIDIA driver version.

（B）Incorrect NCCL configuration, such as an invalid network interface or incorrect device affinity settings.

（C）A faulty network cable connecting the server to the rest of the cluster.

（D）A bug in the NCCL library itself; downgrade to a previous version of NCCL.

（E）Insufficient inter-GPU bandwidth; reduce the batch size to decrease communication overhead.

（A）docker run -gpus device=GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ...

（B）docker run -device=/dev/nvidia0 .

（C）docker run -runtime=nvidia --device=GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ...

（D）docker run -gpus all .

（E）docker run -nvidia-visible-devices GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

（A）Accepts all TCP traffic originating from port 8080.

（B）Accepts all TCP traffic destined for port 8080.

（C）Blocks all TCP traffic on port 8080.

（D）Drops all IJDP traffic destined for port 8080.

（E）Redirects TCP traffic from port 8080 to another port.

（A）Increase the number of storage targets (OSTs) to distribute the data across more devices.

（B）Increase the number of metadata servers (MDSs) and distribute the metadata load across them.

（C）Configure BeeGFS to use a different network protocol with lower overhead.

（D）Enable client-side caching of metadata on the training nodes.

（E）Implement data striping across multiple OSTs.

（A）The PCIe slot is faulty. Solution: Try installing the GPU in a different PCIe slot.

（B）The incorrect GPU drivers are installed or no drivers are installed at all. Solution: Download and install the latest drivers from the NVIDIA website.

（C）The GPU is defective. Solution: Return the GPU to the manufacturer.

（D）The motherboard BIOS/UEFI does not support the GPU. Solution: Update the motherboard BIOS/UEFI to the latest version.

（E）The GPU is not compatible with the operating system. Solution: Reinstall the operating system.

（A）Ensure that the NVLink Switch firmware is compatible with the installed GPUs.

（B）Verify that all NVLink cables are securely connected and properly seated.

（C）Check the Power supply for enough capacity and stability.

（D）Reinstall the operating system.

（E）Check the system BIOS settings to ensure that NVLink is enabled and configured correctly.