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NVIDIA NCP-AII Exam Syllabus Topics:

SectionWeightObjectives
Deployment and Configuration25-30%- Network configuration
- System installation and setup
- Software deployment
- Cluster configuration
AI Infrastructure Fundamentals15-20%- NVIDIA software stack overview
- GPU architecture basics
- Data center infrastructure requirements
- AI and Deep Learning concepts
NVIDIA AI Infrastructure Components25-30%- NVIDIA AI Enterprise software
- NVIDIA networking solutions (Mellanox)
- NVIDIA DGX systems
- Storage solutions for AI workloads
Monitoring and Management15-20%- Performance monitoring
- Resource utilization
- Troubleshooting basics
- NVIDIA management tools
Security and Best Practices10-15%- Security fundamentals
- Compliance considerations
- Operational best practices

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NVIDIA AI Infrastructure Sample Questions (Q175-Q180):

NEW QUESTION # 175
A system administrator wants to configure MIG for seven slices on an H100 GPU in an NVIDIA HGX system. Which command should be used?

Answer: B

Explanation:
mig-parted is used to configure MIG partition layouts on NVIDIA GPUs, including creating a seven-slice MIG configuration on an H100 in an HGX system. It provides a structured way to apply MIG profiles across supported GPUs.


NEW QUESTION # 176
You are deploying NVIDIA DGX H100 systems in a data center. Each rack is configured to support up to 10.2 kW of power consumption per unit. What is the most critical step to validate the power requirements?

Answer: D

Explanation:
DGX H100 power validation must confirm that the rack power distribution can support peak system loads while maintaining redundancy. N+1 capacity ensures the rack can continue operating safely and reliably even if one power path or component fails.


NEW QUESTION # 177
An A1 server exhibits frequent kernel panics under heavy GPU load. 'dmesg' reveals the following error: 'NVRM: Xid (PCl:0000:3B:00): 79, pid=..., name=..., GPU has fallen off the bus.' Which of the following is the least likely cause of this issue?

Answer: C

Explanation:
The error message GPU has fallen off the bus strongly suggests a hardware-related issue with the GPU's connection to the motherboard or its power supply. Insufficient power, a loose riser cable, driver bugs and overclocking can all lead to this. A faulty CPU, while capable of causing system instability, is less directly related to the GPIJ falling off the bus and therefore the least likely cause in this specific scenario.


NEW QUESTION # 178
A server with 8 NVIDIAAIOO GPUs is experiencing an unexpected shutdown under heavy load. The IPMI logs show a 'Power Supply Deasserted' event immediately preceding the shutdown. After replacing the PSU, the issue persists. What is the MOST likely cause of the continued shutdowns?

Answer: B

Explanation:
The 'Power Supply Deasserted' event, even after replacing the PSIJ, strongly suggests that overcurrent protection (OCP) is being triggered. OCP is a safety mechanism that shuts down the PSU if it detects excessive current draw. This is particularly likely with multiple high- power GPUs, as the inrush current during startup can momentarily exceed the PSU's capacity. A driver issue or insufficient memory is less likely to cause this specific event.


NEW QUESTION # 179
When configuring an out-of-core (OOC) HPL burn-in for a 40B matrix on 8x H100 nodes, which environment variable prevents GPU out-of-memory errors while reserving space for drivers?

Answer: B

Explanation:
HPL_OOC_SAFE_SIZE reserves a safety margin of GPU memory for drivers, runtime overhead, and other allocations during out-of-core HPL execution. Setting it prevents the OOC run from consuming all available GPU memory and helps avoid out-of-memory failures during large matrix burn-in tests.


NEW QUESTION # 180
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