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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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
  • 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.

>> Latest NCP-AII Questions <<

Pass Guaranteed NVIDIA - NCP-AII - NVIDIA AI Infrastructure โ€“High Pass-Rate Latest Questions

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NVIDIA AI Infrastructure Sample Questions (Q188-Q193):

NEW QUESTION # 188
You are running a distributed training job across multiple nodes, using a shared file system for storing training dat a. You observe that some nodes are consistently slower than others in reading data. Which of the following could be contributing factors to this performance discrepancy? Select all that apply.

Answer: C,D,E

Explanation:
Network congestion (A) can directly impact the data transfer rate between the slower nodes and the storage. IJneven data distribution (B) means some storage nodes are more heavily loaded, leading to slower response times for nodes accessing data on those overloaded nodes. Insufficient RAM (D) limits the amount of data that can be cached locally, forcing more frequent reads from the slower storage system. CPU architecture (C) primarily affects compute performance, not 1/0. The speed of /tmp (E) is relevant if the training job uses local storage extensively for temporary files, but the question focuses on reading training data from the shared file system.


NEW QUESTION # 189
You are deploying a multi-GPU server for deep learning training. After installing the GPUs, the system boots, but 'nvidia-smi' only detects one GPU. The motherboard has multiple PCle slots, all of which are physically capable of supporting GPUs. What is the most probable cause?

Answer: A

Explanation:
Incorrect BIOS/UEFI settings are the most likely cause when GPUs are physically present but not detected. The BIOS controls PCle lane allocation and slot enabling. Reseating GPUs is a good first step, but if the BIOS is misconfigured, it won't resolve the issue. Insufficient power is also a possibility, but BIOS configuration is more common in initial setup.


NEW QUESTION # 190
You are developing a distributed deep learning application that uses multiple GPUs across several Docker containers running on different physical servers. How do you ensure that each container can access and utilize the GPUs on its respective host?

Answer: B

Explanation:
The most robust solution for distributed GPU utilization is to leverage a container orchestration platform like Kubernetes (B) along with the NVIDIA Container Toolkit. Kubernetes handles scheduling, resource allocation (including GPUs), and networking across multiple nodes.
The NVIDIA Container Toolkit ensures that each container can access the GPUs on its host. While (C) is useful, it's not sufficient for multi-server deployments. Docker Swarm (D) can work but lacks the sophisticated GPU scheduling capabilities of Kubernetes. NFS sharing (A) is unnecessary and can introduce performance bottlenecks. A custom Docker network (E) doesn't directly address GPU access.


NEW QUESTION # 191
You are installing multiple NVIDIA GPUs in a server for a deep learning cluster. To optimally utilize the GPUs, which software component(s) are MANDATORY after the physical installation and driver setup? (Select TWO)

Answer: C,E

Explanation:
The NVIDIA CUDA Toolkit provides the necessary libraries and tools for GPU-accelerated computing. A deep learning framework (TensorFlow or PyTorch) is required to build and train deep learning models that leverage the GPUs. While a web browser and text editor might be useful, they are not mandatory. Spreadsheet applications have no purpose here.


NEW QUESTION # 192
You are installing the operating system as part of the initial setup for a new NVIDIA Base Command Manager cluster. Which two of the following actions are essential for a successful OS installation on the cluster's head node?
Pick the 2 correct responses below.

Answer: B,C

Explanation:
The correct actions are to download and verify the BCM ISO, and to set time zone and NTP synchronization during installation. NVIDIA DGX BasePOD deployment guidance for BCM head nodes instructs administrators to download the appropriate BCM ISO and validate the downloaded file by verifying its checksum. This ensures the installation media has not been corrupted or replaced. The same guidance also expects the head node installation to be performed from valid installation media, often using virtual media through the appliance BMC. Time zone and NTP configuration are essential in cluster control-plane environments because logs, certificates, scheduler events, monitoring data, authentication, and distributed troubleshooting depend on accurate and consistent time. Configuring compute-node PXE boot before the head node OS installation is premature because the head node and BCM services usually provide the management foundation for provisioning compute nodes. Legacy BIOS boot is not the recommended approach for modern DGX and BCM deployments; NVIDIA guidance for appliance preparation shows UEFI boot mode. A reliable BCM control plane starts with validated installation media and correct time synchronization.


NEW QUESTION # 193
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