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NEW QUESTION # 15
You have a server with two NVIDIA GPUs connected via NVLink. You want to verify that NVLink is functioning correctly. Which command(s) or tool(s) can you use to check the NVLink status and bandwidth?
Answer: A,C
Explanation:
'nvidia-smi nvlink -statuS provides a direct overview of the NVLink status, including link speed and errors. 'nvidia-smi topo shows the topology of the GPUs and how they are connected, including NVLink connections. 'Ispci' lists PCl devices but doesn't provide NVLink- specific information. 'nvcc -version' checks the CUDA compiler version. 'nvidia-settings' is a GUI tool that can display some information, but it's less precise than 'nvidia-smr for NVLink status.
NEW QUESTION # 16
You are following the official steps to install the NVIDIA Container Toolkit using a package manager on Ubuntu. After importing the NVIDIA package repository and GPG key, what is the next action?
Answer: C
Explanation:
The NVIDIA Container Toolkit (formerly nvidia-docker2) is the essential middleware that allows Docker, Podman, or Containerd to "see" and utilize the host's GPU hardware. The standard installation workflow on Debian-based systems like Ubuntu involves three core phases: repository configuration, package installation, and runtime configuration. Once the GPG key is added (to ensure package integrity) and the .list file is placed in /etc/apt/sources.list.d/ (to point to the NVIDIA production servers), the local package index must be refreshed via apt-get update. Immediately following this, the administrator must install the toolkit using the command sudo apt-get install -y nvidia-container-toolkit. Rebooting (Option A) is unnecessary at this stage because no kernel modules have been modified yet. Downloading the CUDA Toolkit (Option D) is a separate step; notably, the Container Toolkit allows containers to run CUDA applications even if the host only has the NVIDIA driver installed, making the driver-not the host CUDA toolkit-the primary prerequisite.
NEW QUESTION # 17
A system administrator noticed a failure on a DGX H100 server. After a reboot, only the BMC is available.
What could be the reason for this behavior?
Answer: A
Explanation:
On an NVIDIA DGX system, theBaseboard Management Controller (BMC)is an independent processor that runs even if the main CPU and Operating System fail to load. If a server reboots and the administrator can access the BMC web interface or IPMI console, but the OS (Ubuntu/DGX OS) does not load, the most likely cause is aboot disk failure. The DGX H100 uses NVMe drives in a RAID-1 configuration for the OS boot volume. If both drives in the mirror fail, or if the boot partition becomes corrupted, the system will hang at the BIOS or UEFI prompt, unable to find a bootable device. While failed power supplies (Option D) or network links (Option A) can cause issues, they would typically prevent the BMC from being reachable at all or prevent remote network traffic respectively. A GPU failure (Option C) would not stop the OS from booting; the system would simply boot with a degraded GPU count. Therefore, checking the storage health via the BMC "Storage" logs is the correct diagnostic step.
NEW QUESTION # 18
You are deploying a new A1 inference service using Triton Inference Server on a multi-GPU system. After deploying the models, you observe that only one GPU is being utilized, even though the models are configured to use multiple GPUs. What could be the possible causes for this?
Answer: B,D
Explanation:
The 'instance_group' parameter in the model configuration dictates how Triton distributes the model across GPUs. Without proper configuration, it may default to a single GPIJ. CUDA MPS allows multiple CUDA applications (in this case, Triton inference processes) to share a single GPU, improving utilization. Insufficient CPU cores or non-optimized models could limit performance, but wouldn't necessarily restrict usage to a single GPIJ. While dissimilar GPIJs can affect performance, Triton will attempt to schedule across them if configured correctly.
NEW QUESTION # 19
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: E
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 # 20
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