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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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
  • 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 (Q25-Q30):

NEW QUESTION # 25
A system administrator needs to troubleshoot multiple GPUs in a cluster environment. Which would be the most efficient tool for this?

Answer: A

Explanation:
NVIDIA DCGM is designed for managing and diagnosing GPUs at data center and cluster scale.
It provides health monitoring, diagnostics, telemetry, and validation across multiple GPUs and nodes, making it more efficient than checking each GPU individually.


NEW QUESTION # 26
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?

Answer: D

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 # 27
An AI inference workload running on a server equipped with NVIDIA TensorRT is experiencing intermittent performance degradation. Profiling reveals potential issues with GPU utilization. Which command-line tool is best suited to provide real-time GPU performance metrics, including utilization, memory usage, and power consumption, during workload execution?

Answer: A

Explanation:
'nvidia-smi' (NVIDIA System Management Interface) is the primary tool for monitoring GPU performance in real-time, including utilization, memory usage, and power consumption. While 'nvprof is a profiler, it's more suited for in-depth analysis after the run. 'nvitop' and gpustat are third-party tools that provide similar functionalities as -nvidia-smr. 'tegrastats' is specific to NVIDIA Jetson devices.


NEW QUESTION # 28
During HPL execution on a DGX cluster, the benchmark fails with "not enough memory" errors despite sufficient physical RAM. Which HPL.dat parameter adjustment is most effective?

Answer: D

Explanation:
High-Performance Linpack (HPL) is a memory-intensive benchmark that allocates a large portion of available GPU memory to store the matrix $N$. While a server may have 2TB of physical system RAM, the "not enough memory" error usually refers to theHBM (High Bandwidth Memory)on the GPUs themselves. In a DGX H100 system, each GPU has 80GB of HBM3. If the problem size ($N$) specified in the HPL.dat file is too large, the required memory for the matrix will exceed the aggregate capacity of the GPU memory.
Reducing the problem size ($N$) while maintaining the optimal block size ($NB$) ensures that the problem fits within the GPU memory limits while still pushing the computational units to their peak performance.
Increasing the block size (Option C) would actually increase the memory footprint of certain internal buffers, potentially worsening the issue. Reducing $N$ is the standard procedure to stabilize the run during the initial tuning phase of an AI cluster bring-up.


NEW QUESTION # 29
An organization deploys a Kubernetes AI platform using GPU Operator. Several GPU nodes appear in the cluster, but containers requesting GPU resources remain in a Pending state. The administrator verifies that NVIDIA drivers are correctly installed. Which missing component is the most likely cause?

Answer: D

Explanation:
The NVIDIA Device Plugin advertises GPU resources to Kubernetes so that the scheduler can allocate GPUs to pods. Without it, GPUs remain invisible to Kubernetes even if drivers are installed correctly. DCGM Exporter provides monitoring, Node Feature Discovery adds hardware labels, and CUDA Toolkit inside containers does not expose GPU resources.


NEW QUESTION # 30
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