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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
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
  • 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 4
  • 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 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 (Q69-Q74):

NEW QUESTION # 69
Which of the following is the MOST important reason for using a dedicated storage network (e.g., InfiniBand or RoCE) for AI/ML workloads compared to using the existing Ethernet network?

Answer: C

Explanation:
The primary benefit of a dedicated storage network like InfiniBand or RoCE is the significant improvement in latency and bandwidth (option B) compared to Ethernet. These technologies are designed for high-performance computing and can handle the intense 1/0 demands of AI/ML workloads. While security (A) can be improved through isolation, and QOS (E) is possible, the performance advantage is the most crucial factor. Cost (D) is generally higher, and management (C) can be more complex.


NEW QUESTION # 70
Consider a scenario where you are setting up a high-performance computing cluster with several GPU-accelerated nodes using Slurm as the resource manager. You want to ensure that jobs requesting GPUs are only scheduled on nodes with the appropriate NVIDIA drivers and CUDA toolkit installed. How can you achieve this within Slurm?

Answer: B

Explanation:
Using Slurm's node features is the most straightforward and recommended approach for tagging nodes with specific capabilities. The '-constraint' option allows jobs to request nodes with particular features. GresTypeS can be used, but node features provide more flexibility and control. Installing drivers dynamically is impractical and inefficient. DCGM is primarily for monitoring, not core scheduling requirements.


NEW QUESTION # 71
You are setting up a multi-node A1 cluster with NVIDIA GPUs and InfiniBand for inter-node communication. You need to ensure the InfiniBand network is functioning optimally for GPU-accelerated workloads. What steps would you take to validate the InfiniBand installation and performance?

Answer: A

Explanation:
Sibstat' verifies interface status. 'ibping' and 'ibperf are InfiniBand-specific tools for latency and bandwidth testing. NCCL (NVIDIA Collective Communications Library) is critical for distributed training, and provides valuable diagnostic information. The other options are either incomplete or rely on tools not specific to InfiniBand.


NEW QUESTION # 72
An administrator needs to perform a comprehensive pre-production stress test on a DGX H100 system. Which command validates GPU, CPU, memory, and storage components while following NVIDIA's recommended procedure?

Answer: C

Explanation:
The correct command is sudo nvsm stress-test --force. NVIDIA recommends using NVSM for the DGX pre- flight stress test before putting a DGX H100 system into production or after servicing. The documented NVSM stress test can run checks across supported components, including GPUs, CPU, memory, and storage, and the recommended command for all supported components is sudo nvsm stress-test --force. nvidia-smi -q provides detailed GPU information, but it does not execute a full platform stress test. The Linux stress command can load CPU and I/O subsystems, but it is generic and does not validate the DGX platform using NVIDIA's health model. gpu_burn may stress GPUs, but it does not cover CPU, system memory, storage, and DGX-specific platform checks in the recommended way. During server bring-up, NVSM is preferred because it understands DGX hardware components and can identify platform health issues before the node is released to production workloads.


NEW QUESTION # 73
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: A

Explanation:
The correct answer is mig-parted. NVIDIA MIG Partition Editor, commonly called mig-parted, is designed to automate and manage Multi-Instance GPU partitioning profiles, especially in environments where repeatable MIG layouts are required. An H100 GPU can be divided into multiple isolated GPU instances, and a seven- slice configuration is a common profile pattern for maximizing the number of smaller MIG instances on a single GPU. While nvidia-smi can enable MIG mode and manually create or destroy GPU instances, mig- parted is better suited when an administrator needs to apply defined MIG profiles consistently across HGX systems or multiple nodes. nvcc is the CUDA compiler and has no role in configuring MIG partitions. nvlink- config is not the correct utility for MIG profile configuration. In NVIDIA AI infrastructure, MIG configuration must be controlled carefully because Kubernetes device plugins, GPU Operator, Slurm GRES configuration, and tenant scheduling depend on consistent GPU instance layouts. Using mig-parted helps reduce manual errors and supports repeatable server bring-up for shared inference or multi-tenant AI environments.


NEW QUESTION # 74
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