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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Physical Layer Management	5%	- Networking and GPU partitioning 1. MIG (Multi-Instance GPU) configuration 2. BlueField network platform configuration
Topic 2: Troubleshoot and Optimize	12%	- Fault detection and remediation 1. GPU, fan, network card fault identification 2. Replacement of faulty hardware components - Performance optimization 1. Server performance tuning (Intel/AMD platforms) 2. Storage optimization
Topic 3: Cluster Test and Verification	33%	- Performance and stress testing 1. Cluster burn-in tests (HPL, NCCL, NeMo) 2. HPL (High-Performance Linpack) benchmarking 3. NCCL communication testing 4. Single-node stress testing - Network and hardware validation 1. Cabling and signal verification 2. NVLink validation 3. Firmware validation (switches, transceivers, BlueField) - Cluster diagnostics 1. ClusterKit multi-node assessment 2. Storage testing
Topic 4: System and Server Bring-up	31%	- Deployment and validation lifecycle 1. Network topologies for AI factories 2. Sequence of deployment and validation events - Hardware initialization and configuration 1. Firmware upgrades including HGX and fault detection 2. Hardware validation for workloads 3. BMC, OOB, and TPM initial configuration 4. GPU server installation and validation - Physical infrastructure validation 1. Cable and transceiver types validation 2. Power and cooling validation 3. Storage parameter initialization
Topic 5: Control Plane Installation and Configuration	19%	- Infrastructure software stack deployment 1. Cluster setup (Slurm, Enroot, Pyxis) 2. Operating system installation 3. Base Command Manager (BCM) installation and HA configuration - GPU utilization in containers 1. Docker GPU usage validation - Drivers and toolkits 1. NVIDIA GPU and DOCA drivers installation/update 2. NVIDIA container toolkit installation 3. NGC CLI deployment

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NVIDIA AI Infrastructure NCP-AII Prüfungsfragen mit Lösungen (Q91-Q96):

91. Frage
A cluster administrator needs to validate transceiver firmware versions across 200 ports using UFM. Which GUI-based method provides a consolidated view?

A. Navigate to 'Devices " > select a switch > " Cables ' tab to see ASIC firmware and transceiver versions.
B. Run mlxlink -d lid- < LID > -m on each port manually.
C. Export all switch logs and grep for 'FW Version " .
D. Use " Topology' view to visually inspect cable icons.

Antwort: A

Begründung:
Managing a large-scale AI fabric requires centralized visibility into the physical layer. The NVIDIA Unified Fabric Manager (UFM) provides a comprehensive Dashboard for InfiniBand networks. To check transceiver firmware-which is critical for ensuring feature parity and stability across the fabric-the administrator can use the UFM Enterprise GUI. By navigating to the " Devices " section and selecting a specific switch, the " Cables " tab will aggregate telemetry for every occupied port. This view displays the manufacturer, part number, and the specific firmware version of the transceivers (LinkX) or Active Optical Cables (AOC). This consolidated view is far more efficient than manual CLI queries (Option C) for 200+ ports. Maintaining uniform firmware across transceivers ensures that optimizations like Adaptive Routing and Congestion Control perform consistently across the entire 400G or 200G fabric.

92. Frage
An AI operations team investigates unexpectedly slow distributed training. GPU utilization averages only 55%, while storage, CPU, and InfiniBand monitoring all indicate normal performance. DCGM reports no hardware faults, but profiling shows GPUs frequently waiting during synchronization phases. Which area should the engineers investigate first?

A. CPU thermal throttling
B. MIG partition sizing
C. NCCL communication configuration and topology awareness
D. NVMe drive firmware compatibility

Antwort: C

Begründung:
When GPU utilization drops primarily during synchronization despite healthy hardware and storage performance, inefficient collective communication is a likely cause. NCCL configuration-- including transport selection, topology detection, and network optimization--should be examined first. Storage firmware, CPU thermals, and MIG configuration are less likely to produce synchronization-specific delays in this scenario.

93. Frage
After installing a new NVIDIA GPU, you attempt to run a CUDA application, but you encounter the following error: 'CUDA error: CUDA driver version is insufficient for CUDA runtime version'. You have verified the driver and CUDA toolkit are installed. What is the MOST likely reason for this error, and how do you resolve it?

A. The CUDA runtime libraries are missing from the system path. Add them to the PATH variable.
B. The CUDA toolkit is too old. Update the CIJDA toolkit.
C. The NVIDIA driver is too old for the CUDA toolkit. Update the NVIDIA driver to a version that supports the CUDA toolkit.
D. The CUDA VISIBLE DEVICES environment variable is not set correctly.
E. The GPU is not compatible with the CUDA toolkit. Install a different GPIJ.

Antwort: C

Begründung:
This error indicates an incompatibility between the driver and the CUDA toolkit. The most common reason is an outdated driver. The driver must be at least as new as the CUDA toolkit's minimum required driver version. CUDA VISIBLE DEVICES relates to GPU selection, not driver version.

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

Antwort: B

Begründung:
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.

95. Frage
A system administrator needs to replace a failing GPU. Which of the following commands should be used to check slot capabilities?

A. netstat -tulpn
B. dmesg
C. nvidia-smi
D. lspci

Antwort: D

Begründung:
lspci is used to inspect PCIe devices and slot-related capabilities, including GPU detection, PCIe link width, link speed, and bus placement. This helps verify the slot characteristics before replacing or troubleshooting a GPU.

96. Frage
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