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| Topic | Details |
|---|
| Topic 1 | - Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
|
| Topic 2 | - System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC
- OOB
- TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
|
| Topic 3 | - Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm
- Enroot
- Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.
|
| Topic 4 | - Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
|
| Topic 5 | - Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD
- Intel servers and storage.
|
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NVIDIA AI Infrastructure Sample Questions (Q137-Q142):
NEW QUESTION # 137
You are tasked with installing the NGC CLI on a host that does not have direct internet access. You have downloaded the NGC CLI package to a local repository. Which of the following steps are required to successfully install and configure the NGC CLI in this offline environment?
- A. Transfer the NGC CLI package to the host and install it using 'pip install .whl'.
- B. Configure the NGC CLI to point to your local package repository by setting the environment variable.
- C. Run 'ngc config set' to configure the API key, pointing to a local configuration file.
- D. Manually download and install all dependencies of the NGC CLI package using 'pip install --no-index --find-links=/path/to/dependencies .whl'.
- E. Only copying the whl file is sufficient, NGC CLI dependencies are always local
Answer: A,B,C,D
Explanation:
In an offline environment, you need to install the package locally (A), configure the CLI to know where to find the package (B), manually install dependencies (C), and configure the API key (D). Option E is wrong because dependencies must be handled manually in the offline environment.
NEW QUESTION # 138
Your company is planning to expand its AI capabilities significantly over the next five years. To future-proof your storage infrastructure, you need a solution that can scale in both capacity and performance. Which of the following strategies best ensures that your storage infrastructure remains adaptable to future AI demands?
- A. Implement single-tier cloud storage solution to leverage cloud scalability.
- B. Deploy an all-flash array and remove data tiering to reduce latency.
- C. Use a hybrid cloud model combining scalable cloud resources with on-premises infrastructure.
- D. Implement on-premises block storage system with periodic hardware upgrades.
Answer: C
Explanation:
A hybrid cloud model provides the flexibility to scale capacity and performance over time by combining on-premises infrastructure for predictable, high-performance workloads with cloud resources for elastic growth. This approach keeps the storage architecture adaptable as AI data volumes, training workloads, and performance requirements increase.
NEW QUESTION # 139
An engineer is reimaging a DGX system in a large cluster. Which method ensures the most efficient and secure remote installation without physical access?
- A. Use apt-get to upgrade the operating system without rebooting the system.
- B. Skip ISO verification and directly flash the operating system to the disk via SSH.
- C. Build a software image on Base Command Manager and then reimage the system.
- D. Create a USB drive with the ISO and manually boot from it on the DGX system.
Answer: C
Explanation:
Building a software image in NVIDIA Base Command Manager and using it to reimage the DGX system is the most efficient and secure method in a large cluster. In managed DGX infrastructure, remote image provisioning allows administrators to maintain consistent operating system versions, drivers, CUDA components, network settings, security baselines, and cluster management agents across compute nodes. This avoids manual access to each server and reduces configuration drift. Using apt-get to upgrade the operating system is not equivalent to a controlled reimage and may leave inconsistent packages or unsupported driver combinations. Creating a USB drive requires physical access and does not scale for large deployments.
Skipping ISO verification and directly flashing the operating system over SSH is unsafe because it bypasses image validation and can leave nodes in an unrecoverable or untrusted state. BCM-based imaging supports repeatable deployment, centralized control, rollback planning, and integration with node categories or software images. This is the preferred operational model for reliable DGX cluster bring-up and lifecycle management.
NEW QUESTION # 140
A system administrator needs to configure a BlueField DPU and enable RShim on the baseboard management controller (BMC). Which command should be executed?
- A. systemctl enable bmc-rshim.service
- B. scp <path_to_bfb> root@<bmc_ip>:/dev/rshim0/boot</bmc_ip></path_to_bfb>
- C. ipmitool raw 0x32 0x6a 1
- D. systemctl restart rshim
Answer: C
Explanation:
The ipmitool raw 0x32 0x6a 1 command enables RShim access through the BMC for BlueField DPU configuration. This allows the BMC to expose the RShim interface needed for DPU provisioning and management.
NEW QUESTION # 141
A system administrator is working on a DGX A100 and needs to start a CUDA job. The job fails and a cudaErrorSystemNotReadyerror is returned. What is the cause of this error?
- A. A GPU fails to register with the fabric.
- B. There are too many running CUDA jobs.
- C. CUDA is not properly installed.
- D. FM is not completely done initializing.
Answer: D
Explanation:
On a DGX A100 with NVSwitch, CUDA workloads depend on Fabric Manager completing NVSwitch/NVLink fabric initialization. If a CUDA job starts before Fabric Manager is fully initialized, the system can return cudaErrorSystemNotReady because the GPU fabric is not ready for CUDA execution.
NEW QUESTION # 142
......
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