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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 5
  • 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.

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NVIDIA AI Infrastructure Sample Questions (Q30-Q35):

NEW QUESTION # 30
For an NVIDIA Enterprise AI Factory with 256 GPUs, which storage solution characteristic is most critical to validate during scaling tests?

Answer: A

Explanation:
Scaling an AI cluster to 256 GPUs (32 nodes of DGX H100) creates a massive " Incast " problem for the storage fabric. During large-scale training, every node frequently reads huge batches of data simultaneously.
NVIDIA's reference architectures (BasePOD/SuperPOD) specify that for high-performance training, each node must be able to sustain a minimum throughput-often 8 GiB/s or more-to keep all 8 GPUs saturated. If the storage system can handle one node at high speed but chokes when all 32 nodes request data, the " Scaling Efficiency " of the AI model will drop drastically as GPUs sit idle waiting for IO. Therefore, validating consistent per-node throughput under full cluster load is the most critical metric for an AI Factory. While IOPS (Option D) are important for small files, modern AI datasets are often sharded into large binary formats (like WebDataset or TFRecord) where sequential throughput becomes the primary bottleneck.


NEW QUESTION # 31
Which of the following are key considerations when choosing between CPU pinning and NUMA (Non-Uniform Memory Access) awareness for a distributed training job on a multi-socket AMD EPYC server with multiple GPUs?

Answer: B,D

Explanation:
CPU pinning and NUMA awareness are both important for optimizing performance on multi-socket systems. CPU pinning reduces context switching overhead, while NIJMA awareness reduces memory access latency. Using them together provides the best performance. They ensure the CPU and memory being used are on the same node, which reduces latency. While GPUs handle intense computation, CPUs prepare and feed data to the GPUs, and are critical for performance.


NEW QUESTION # 32
You are tasked with configuring an NVIDIAA100 GPU for a multi-tenant environment using MIG. The goal is to provide the following MIG configuration:

Answer: A

Explanation:
The question specifies that you need the configuration that allows 7x1g.10gb instances and one 7g.80gb. The format is num_1g/num_2g/num_3g/num_4g/num_7g/num_10g/num_20g/num_40g. In the provided configuration, 0/1/2/3/4/5/6 means one 7g, then we need the instances.


NEW QUESTION # 33
You have configured MIG on your A100 GPU, creating several MIG instances. You now want to allocate a specific MIG instance to a Docker container. How would you specify the necessary device option when running the 'docker run' command to ensure the container uses only that MIG instance? Assuming the MIG instance UUID is GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

Answer: E

Explanation:
The correct way to specify a specific MIG instance for a Docker container is using the '-gpus device=' option with the MIG instance UUID. This ensures the container only has access to the designated MIG instance. '-gpus all' grants access to all GPUs. "-device=/dev/nvidiaC provides access to the entire GPU. '--runtime=nvidia' is required for NVIDIA GPU support but doesn't specify a particular instance. There is no option unvidia-visible-devices'.


NEW QUESTION # 34
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: A

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 # 35
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