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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
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	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	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 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	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.

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NVIDIA AI Infrastructure Sample Questions (Q63-Q68):

NEW QUESTION # 63
A researcher wants to allow a container to use only two specific GPUs out of four available ones (GPU 1 and GPU 2) on the host. The system is already set up for NVIDIA Docker with the container toolkit. Which Docker command will correctly assign only these GPUs to the container?

A. docker run --rm --gpus '"device=1,2"' nvidia/cuda nvidia-smi
B. docker run --gpus '"device=0,3"' nvidia/cuda nvidia-smi --query-gpu=uuid --format=csv
C. docker run --gpus '"device=0,1"' nvidia/cuda nvidia-smi --query-gpu=uuid --format=csv
D. docker run --rm --gpus 2 nvidia/cuda nvidia-smi

Answer: A

Explanation:
The Docker --gpus option can restrict a container to specific GPU IDs. Specifying device=1,2 exposes only GPU 1 and GPU 2 to the container, and running nvidia-smi inside the CUDA container verifies that only those GPUs are visible.

NEW QUESTION # 64
An engineer needs to completely remove NVIDIA GPU drivers from an Ubuntu 22.04 system to troubleshoot conflicts. Which command sequence ensures all driver components are purged?

A. sudo apt-get purge nvidia-* && sudo apt-get autoremove
B. sudo apt-get remove nvidia-driver-550
C. sudo ubuntu-drivers uninstall
D. sudo rm -rf /usr/lib/nvidia

Answer: A

Explanation:
Purging nvidia-* removes NVIDIA driver packages and related configuration files, while autoremove cleans up unused dependencies and kernel module packages left behind. This provides a more complete cleanup than removing only a single driver package.

NEW QUESTION # 65
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?

A. Reduced cost compared to upgrading the existing Ethernet infrastructure.
B. Automatic Quality of Service (QOS) prioritization for AI/ML traffic.
C. Lower latency and higher bandwidth for data transfer.
D. Simplified network management and configuration.
E. Improved security due to network isolation.

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 # 66
You're deploying a BlueField-2 DPU in a cloud environment and need to ensure the integrity of the DPU's firmware. You want to verify that the firmware hasn't been tampered with. Which of the following methods provides the strongest level of assurance for firmware integrity?

A. Verifying the SHA256 checksum of the firmware image against a known good value provided by NVIDIA.
B. Using a digitally signed firmware image and verifying the signature using NVIDIA's public key.
C. Comparing the firmware version reported by the DPU with the version listed in the NVIDIA release notes.
D. Checking the file size of the firmware image against a known good value.
E. Checking the MD5 checksum of the firmware image against a known good value.

Answer: B

Explanation:
Digitally signed firmware provides the strongest guarantee of integrity. The signature verifies that the firmware hasn't been tampered with since it was signed by NVIDIA. SHA256 checksums are good, but digital signatures are cryptographically stronger. MD5 checksums are considered weak and easily compromised. Firmware version and file size offer minimal assurance against sophisticated attacks.

NEW QUESTION # 67
An engineer needs to validate NVLink Switch functionality on a DGX H100 system with 8 GPUs. Which NCCL command verifies intra-node NVLink bandwidth?

A. all_reduce_perf -b 8 -e 16G -f 2 -g 4 with NCCL_TESTS_SPLIT="MOD 2"
B. all_reduce_perf -b 8 -e 16G -f 2 -g 8 with NCCL_TESTS_SPLIT="OR 0x7"
C. broadcast_perf -b 8 -e 16G -f 2 -g 8 without split configuration
D. all_reduce_perf -b 8 -e 16G -f 2 -g 1 repeated 8 times

Answer: B

Explanation:
The NVIDIA Collective Communications Library (NCCL) "Tests" are used to verify the maximum achievable bandwidth of the interconnects. On a DGX H100, the GPUs are connected via a dedicated high- bandwidthNVLink Switch fabric(NVLink 4), which provides significantly higher throughput than PCIe. To validate the intra-node (within a single server) performance, the all_reduce_perf test is used. The command in Option D is specifically designed to stress all 8 GPUs (-g 8) across a wide range of message sizes (8 bytes to
16G). The use of the environment variable NCCL_TESTS_SPLIT with the bitwise "OR" or "AND" masks allows the engineer to isolate specific traffic patterns or groups of GPUs to ensure the NVLink switches are distributing the load evenly. For a standard 8-GPU H100 tray, achieving a "Bus Bandwidth" of ~450 GB/s to
900 GB/s (depending on the precision and message size) confirms that the NVLink fabric is operating at its theoretical peak. Using only 4 GPUs (Option B) or 1 GPU (Option C) would not provide a complete picture of the NVLink switch bisection bandwidth.

NEW QUESTION # 68
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