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NVIDIA AI Infrastructure Sample Questions (Q93-Q98):

NEW QUESTION # 93
Consider a scenario where you are using NCCL (NVIDIA Collective Communications Library) for multi-GPU training across multiple servers connected via NVLink switches. Which NCCL environment variable would you use to specify the network interface to be used for communication?

Answer: B

Explanation:
is the correct environment variable to specify the network interface used by NCCL. Is for Infiniband, and the other options are not directly related to specifying the network interface.


NEW QUESTION # 94
Which of the following are valid methods for verifying the health and connectivity of InfiniBand links in an NCP-AII environment? (Select TWO)

Answer: A,D

Explanation:
'ibstat' is a command-line utility specifically designed for checking the status of InfiniBand ports. 'sminfo' allows you to communicate with the Subnet Manager and retrieve network topology and status. While 'ping' can verify IP connectivity over InfiniBand, it doesn't directly assess the health of the InfiniBand link itself. Checking system logs is a useful supplementary task but isn't the primary method.


NEW QUESTION # 95
You are designing an AI infrastructure cluster for training large language models (LLMs). The dataset consists of 10TB of image data and 5TB of text dat a. You estimate that intermediate training data (checkpoints, temporary files) will require an additional 20TB of storage. You want to use a parallel file system for optimal performance. Considering a replication factor of 2 for data redundancy and a 20% overhead for file system metadata, what is the minimum raw storage capacity you should provision?

Answer: D

Explanation:
Total data size: IOTB + 5TB + 20TB = 35TB. With a replication factor of 2, the storage required is 35TB 2 = 70TB. Adding 20% overhead for metadata, we get 70TB 1.2 = 84 T B. Therefore, the minimum raw storage capacity is 84 + 8.4 = 92.4 TB. Overhead needs to be calcualted from after replication is implemented, so replication + 20% overhead.


NEW QUESTION # 96
After successfully installing the NVIDIA Container Toolkit and configuring Docker, you're attempting to build a container image that leverages the GPU. You're using a Dockerfile but encounter the following error during the 'docker build' process: 'error during connect: this error may indicate that the docker daemon is not running'. However, the Docker daemon IS running. What is the most likely reason the build process is failing to connect, specifically in the context of GPU-enabled containers?

Answer: E

Explanation:
The error 'error during connect: this error may indicate that the docker daemon is not running' during a 'docker build', when the daemon actually is running, can indicate a failure to connect to the daemon for a specific reason related to GPU access. 'docker build' requires - gpus all' to be passed in order for CUDA to correctly build the image. Permissions(A) are unlikely to cause this specific connection error. User group issues(B) are usually related to running containers, not building them. A networking issue (D) is possible but less likely in the context of a local build. The same can be said for container exceeding the hosts' available memory.


NEW QUESTION # 97
You are setting up a BlueField-2 SmartNIC and want to offload network functions. Which of the following are valid methods for enabling hardware offload capabilities?

Answer: B,C

Explanation:
The 'ethtoor command is used to configure various network interface settings, including enabling/disabling hardware offload features. Installing the correct Mellanox OFED drivers is crucial, as they provide the necessary modules and tools to utilize the hardware offload capabilities. While device tree modification can influence hardware behavior, it's less common and typically handled by driver configuration. A custom script directly programming the hardware is unlikely and driver recompilation may be required, but often isn't necessary with default settings.


NEW QUESTION # 98
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