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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
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
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3
  • 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 4
  • 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 5
  • 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.

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NVIDIA AI Infrastructure Sample Questions (Q179-Q184):

NEW QUESTION # 179
You are managing a cluster of GPU servers for deep learning. You observe that one server consistently exhibits high GPU temperature during training, causing thermal throttling and reduced performance. You've already ensured adequate airflow. Which of the following actions would be MOST effective in addressing this issue?

Answer: B,E

Explanation:
Re-seating the GPU (D) ensures a proper connection between the GPU and the motherboard, which is crucial for effective heat dissipation. Increasing fan speed (E) can directly improve cooling. Lowering the power limit (B) reduces temperature but also reduces performance. Updating drivers (C) may help in some cases, but it is less likely to solve a thermal throttling problem. Lowering the ambient temperature (A) is generally beneficial but might not be specific enough to fix the overheating issue on a single server.


NEW QUESTION # 180
During a 72-hour HPL burn-in test on a DGX H100 cluster, one node shows a 15% performance drop after 48 hours. What are the two most likely causes and diagnostic steps?
Pick the 2 correct responses below.

Answer: A,D

Explanation:
The two most likely causes are network packet loss and thermal throttling. A performance drop after 48 hours of HPL burn-in is less likely to be a simple launch-time MPI configuration issue, because MPI affinity errors usually appear from the beginning of the run as consistently poor performance. A delayed degradation suggests the system changed state under sustained load. Thermal throttling is a common cause: after many hours, rack cooling imbalance, blocked airflow, high inlet temperature, or fan behavior can cause GPU clocks to drop. nvidia-smi dmon helps monitor GPU temperature, power, utilization, and clocks over time. Network packet loss is also likely in multi-node HPL because HPL depends on heavy communication across the InfiniBand fabric. Link errors, symbol errors, retransmissions, degraded cables, or congestion can reduce sustained performance. ibdiagnet is the correct fabric-level diagnostic tool to collect and analyze InfiniBand health, topology, counters, and link issues. Rebooting and reducing matrix size would hide the symptom rather than diagnose it. Correct burn-in practice is to preserve evidence, inspect thermal telemetry, review network diagnostics, and compare the affected node against healthy peers.


NEW QUESTION # 181
Which of the following techniques can be used to optimize storage performance for deep learning training?

Answer: A,C,D

Explanation:
Data prefetching anticipates future data needs and loads data into the cache before it is requested. A larger block size can improve I/O throughput for large files. Data sharding distributes data across multiple storage devices to increase parallelism. Data compression, while saving space, can add overhead during training. Data deduplication is not normally usefull for training data sets.


NEW QUESTION # 182
During multi-node HPL burn-in, GPUs show uneven utilization. Which configuration ensures balanced workload distribution?

Answer: B

Explanation:
Setting GPU and CPU affinity aligns each GPU with the appropriate CPU cores and NUMA locality, helping distribute work evenly across the node. This prevents imbalance caused by poor process placement, cross-socket traffic, or mismatched GPU-to-CPU binding during multi-node HPL burn-in.


NEW QUESTION # 183
A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. 'nvidia-smi' reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others. What is the MOST likely cause and the immediate action to take?

Answer: A

Explanation:
Elevated inlet temperatures, despite normal GPU temperatures, strongly suggest an airflow issue. GPUs 3 and 4 are likely positioned in a way that restricts airflow. The first step is to check fan speeds and for any physical obstructions blocking airflow. Replacing components without addressing the airflow issue will not solve the problem.


NEW QUESTION # 184
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