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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 | - 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.
|
| 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 | - 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 5 | - Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
|
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NVIDIA AI Infrastructure Sample Questions (Q14-Q19):
NEW QUESTION # 14
You're deploying a multi-GPU training job on a cluster using Slurm. You need to ensure that the GPUs allocated to the job are healthy and functioning correctly before the training starts. What's the MOST effective approach to pre-validate the GPU hardware?
- A. Monitor the GPU temperature using 'nvidia-smi' during the first few minutes of the training job.
- B. Run a simple CUDA vector addition program on each GPU and check for errors.
- C. Allocate all available GPUs to the job and assume they are healthy.
- D. Check the output of 'nvidia-smi' to ensure all GPUs are listed and have the expected memory.
- E. Execute the NVIDIA Data Center GPU Manager (DCGM) diagnostic suite on the allocated GPUs.
Answer: E
Explanation:
Using the DCGM diagnostic suite is the most thorough way to pre-validate GPU hardware. DCGM provides comprehensive tests to check GPU health, including memory, compute, and interconnects. A simple CUDA program or checking nvidia-smr provides basic validation, but not as comprehensive as DCGM. Monitoring temperature is reactive, not proactive. Assuming GPUs are healthy without validation is risky.
NEW QUESTION # 15
You're experiencing unexpected memory errors during the execution of a large-scale deep learning training job. Suspecting faulty memory modules, which diagnostic tool is best suited to perform a comprehensive memory test on the server's RAM?
- A. nvidia-smi
- B. Ishw
- C. memtest86+
- D. nvprof
- E. DCGM
Answer: C
Explanation:
'memtest86+' is specifically designed for performing thorough memory tests on system RAM, identifying potential errors and hardware faults. 'nvidia-smi' is for GPU monitoring, 'nvprof is a profiler, DCGM can test GPU memory, but not system RAM specifically. 'Ishw' lists hardware information, but doesn't perform diagnostic tests.
NEW QUESTION # 16
You are developing a CI/CD pipeline for deploying A1 models to a Kubernetes cluster with NVIDIA GPUs. Your pipeline includes steps to build, test, and deploy the model as a containerized application. What is the recommended approach for ensuring that the application container image includes the correct NVIDIA drivers and libraries?
- A. Downloading the latest drivers from NVIDIA's website and adding them to the container image.
- B. Using a base image provided by NVIDIA that includes the necessary drivers and libraries.
- C. Relying on the NVIDIA Container Toolkit to automatically install the drivers and libraries at runtime.
- D. Manually installing the NVIDIA drivers and libraries inside the container image during the build process.
- E. Configuring the cluster to download and configure the needed drivers just before deploying your application.
Answer: B
Explanation:
Using a base image provided by NVIDIA (such as those from NGC) is the recommended approach because it ensures that the container image includes the correct and compatible drivers and libraries, simplifying the build process and reducing potential compatibility issues. The NVIDIA Container Toolkit requires host drivers and handles the injection of the library into the container and not the building of the image.
NEW QUESTION # 17
After NCCL burn-in reports "transport retry count exceeded", which corrective action addresses the underlying fabric issue?
- A. Inspect InfiniBand link quality metrics (BER, symbol errors) and replace faulty cables
- B. Switch from Ring to Tree algorithms via NCCL_ALGO=TREE
- C. Increase NCCL_IB_TIMEOUT to tolerate longer latencies
- D. Reduce message size to decrease network utilization
Answer: A
Explanation:
A "transport retry count exceeded" error during NCCL burn-in usually points to an unreliable InfiniBand fabric path, such as bad cables, poor signal quality, symbol errors, or excessive bit errors. Checking link-quality counters and replacing faulty cables addresses the underlying physical fabric problem rather than masking it.
NEW QUESTION # 18
You are troubleshooting a network performance issue in your NCP-AII environment. After running 'ibstat' on a host, you see the following output for one of the InfiniBand ports:

What does the 'LMC: 0' indicate, and what are the implications for network performance?
- A. LMC : 0 indicates the port is operating at the lowest possible speed.
- B. LMC: 0 indicates that the link is down and not functioning correctly.
- C. LMC : 0 indicates that the Subnet Manager is not running correctly.
- D. LMC : 0 is the default and expected value; it has no impact on performance.
- E. LMC: 0 indicates that Link Aggregation (LAG) is not enabled on this port, meaning only a single link is being used for communication.
Answer: E
Explanation:
LMC (Link Mask Capability) indicates the level of link aggregation enabled. LMC: 0 means that link aggregation is disabled. This means that only one link is being used for communication, which could limit the overall bandwidth and potentially create a bottleneck if multiple GPUs are trying to communicate through that single link. Higher LMC values indicate that multiple links are aggregated together to increase bandwidth.
NEW QUESTION # 19
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