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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA AI Infrastructure (NCP-AII) Certification Exam |
| Exam Number: | NCP-AII |
| Related Certifications: | NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple-choice, Scenario-based questions |
| Real Exam Qty: | 70-75 |
| Certificate Validity Period: | 2 years |
| Exam Price: | $400 |
| Recommended Training: | AI Infrastructure & Operations Fundamentals (NVIDIA Training) AI Infrastructure Professional Workshop |
| Exam Registration: | Official NVIDIA Certification Portal NVIDIA AI Infrastructure Certification Page |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online proctored exam (remote) or authorized test center depending on region |
| Pre Condition: | Recommended 2โ3 years of experience working in data center environments with NVIDIA hardware solutions (GPU servers, networking, storage). |
| Official Syllabus URL: | https://www.nvidia.com/en-eu/learn/certification/ai-infrastructure-professional/ |
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NEW QUESTION # 101
A system engineer needs to set the vGPU scheduling behavior for all GPUs to share the scheduling equally with the default time slice length. What command should be used?
Answer: C
Explanation:
The RmPVMRL=0x00 setting configures the NVIDIA vGPU scheduler to use equal-share scheduling with the default time slice length. The esxcli system module parameters set -m nvidia command is the correct ESXi command format for setting NVIDIA kernel module parameters globally for the GPUs.
NEW QUESTION # 102
An infrastructure engineer in an AI factory has successfully replaced a power supply unit on an NVIDIA DGX H100. After installation, both the IN and OUT LEDs on the new power supply illuminate solid green.
Which NVSM CLI command should the engineer use to quickly verify the overall system status and ensure it is operating as expected?
Answer: A
Explanation:
The NVIDIA System Management (NVSM) tool is the definitive CLI utility for monitoring the health of DGX platforms. While replacing a PSU (Power Supply Unit) is a common maintenance task, verifying that the new component is correctly integrated into the system's health model is mandatory. While nvsm show power would provide specific data regarding wattage and voltage for the PSU, the most comprehensive way to ensure the replacement hasn ' t caused secondary issues or that the system hasn ' t remained in a " Degraded
" state is to run nvsm show health. This command performs a global check across all subsystems: GPUs, NVLink switches, storage, fans, and power. If the PSU replacement was successful and the system is back to full redundancy, nvsm show health will return a " Healthy " status. In an AI factory setting, where DGX H100 nodes pull significant power, ensuring that all 6 PSUs (in an N+N or N+1 configuration) are not only physically green but logically acknowledged by the Baseboard Management Controller (BMC) is critical for preventing unexpected shutdowns during high-load training iterations.
NEW QUESTION # 103
You've replaced a faulty NVIDIA Quadro RTX 8000 GPU with an identical model in a workstation. The system boots, and 'nvidia-smi' recognizes the new GPU. However, when rendering complex 3D scenes in Maya, you observe significantly lower performance compared to before the replacement. Profiling with the NVIDIA Nsight Graphics debugger shows that the GPU is only utilizing a small fraction of its available memory bandwidth. What are the TWO most likely contributing factors?
Answer: B,C
Explanation:
Low memory bandwidth utilization after replacing a GPU suggests a bottleneck in data transfer or power delivery. A lower PCle link speed (A) would severely limit the GPU's ability to receive data from the CPU and system memory, resulting in underutilization. Setting the power plan to 'Power Saver' (C) restricts the GPU's power budget, preventing it from reaching its maximum clock speed and memory bandwidth. While OptiX denoiser configuration (B) and scene file issues (D) can impact rendering performance, they are less likely to directly cause low memory bandwidth utilization. VBIOS issues (E) could cause problems, but incorrect or missing VBIOS can cause driver errors or complete system unresponsiveness.
NEW QUESTION # 104
You suspect a faulty NVIDIA ConnectX-6 network adapter in a server used for RDMA-based distributed training. Which commands or tools can you use to diagnose potential issues with the adapter's hardware and connectivity?
Answer: A,C,D,E
Explanation:
All options except E are relevant for diagnosing network adapter issues. 'Ispci -v' (A) verifies hardware detection. 'ibstat' (B) checks InfiniBand-specific details. 'ethtoor (C) examines Ethernet settings. 'ping' (D) tests basic connectivity. 'nvsmimonitord' (E) focuses on GPU monitoring, not network adapters.
NEW QUESTION # 105
During multi-node HPL burn-in, GPUs show uneven utilization. Which configuration ensures balanced workload distribution?
Answer: D
Explanation:
Uneven GPU utilization in a multi-node cluster is a classic symptom ofPCIe/NUMA imbalance. In a DGX H100, each set of GPUs is physically closer to a specific CPU socket and a specific set of high-speed NICs. If the workload (HPL) is launched without strict affinity, an MPI rank running on a CPU core attached to Socket
0 might attempt to control a GPU attached to Socket 1. This forces data to cross the inter-processor links (UPI
/QPI), which have significantly higher latency and lower bandwidth than a direct PCIe path. This
"bottlenecking" causes some GPUs to wait for data longer than others, leading to the uneven utilization observed. The verified solution is to use an orchestration script or flags that enforceAffinity. By setting --gpu- affinity and --cpu-affinity, the administrator ensures that each GPU is managed by a CPU core on its local NUMA node. This alignment minimizes latency and ensures that every GPU in the cluster receives data at the same rate, resulting in the flat, high-utilization profile required for a successful HPL burn-in record.
NEW QUESTION # 106
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