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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA AI Infrastructure (NCP-AII) Certification Exam |
| Exam Number: | NCP-AII |
| Exam Format: | Scenario-based questions, Multiple-choice |
| Real Exam Qty: | 70-75 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Related Certifications: | NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Professional AI Operations (NCP-AIO) |
| Available Languages: | English |
| Exam Price: | $400 |
| Recommended Training: | AI Infrastructure Professional Workshop AI Infrastructure & Operations Fundamentals (NVIDIA Training) |
| Exam Registration: | NVIDIA AI Infrastructure Certification Page Official NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online proctored exam (remote) or authorized test center depending on region |
| Pre Condition: | Recommended 2–3 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 # 78
You're deploying a large language model for inference using NVIDIA Triton Inference Server. You need to validate that the server can handle the expected query load while maintaining acceptable latency. Which tools and metrics are most relevant for this validation?
Answer: D
Explanation:
Validating Triton's performance requires a comprehensive approach. Monitoring GPU usage with 'nvidia-smi', tracking Triton's internal metrics, using load testing tools to simulate real-world traffic, and monitoring CPU utilization are all important for identifying bottlenecks and ensuring optimal performance.
NEW QUESTION # 79
An infrastructure architect is selecting a network technology for a dedicated AI training cluster requiring extremely low latency, adaptive routing, congestion control, and efficient collective communications across thousands of GPUs. Which networking solution is generally the preferred choice for this environment?
Answer: A
Explanation:
InfiniBand is specifically designed for high-performance computing and AI clusters. It provides RDMA, adaptive routing, congestion management, hardware offloads, and very low latency, making it well suited for large-scale distributed AI training. Standard Ethernet lacks these capabilities unless enhanced with RoCE, while Fibre Channel and Wi-Fi are unsuitable for distributed GPU communication.
NEW QUESTION # 80
You are running a Docker container with GPU support using 'nvidia-docker run'. The containerized application unexpectedly fails to detect the GPU. What is the most likely cause?
Answer: B
Explanation:
When running containers that need GPU access, it's essential to explicitly request the GPU resources. The '-gpus all' or '-gpus device=..: flag passed to 'docker run' with the NVIDIA runtime allows the container access to the available GPUs. Without this flag, the container operates as if no GPUs are available. Options A, B, C and D, while potentially problematic, are not the most likely cause if 'nvidia-docker run' was used previously.
NEW QUESTION # 81
An administrator needs to perform a comprehensive pre-production stress test on a DGX H100 system. Which command validates GPU, CPU, memory, and storage components while following NVIDIA's recommended procedure?
Answer: D
Explanation:
The correct command is sudo nvsm stress-test --force. NVIDIA recommends using NVSM for the DGX pre- flight stress test before putting a DGX H100 system into production or after servicing. The documented NVSM stress test can run checks across supported components, including GPUs, CPU, memory, and storage, and the recommended command for all supported components is sudo nvsm stress-test --force. nvidia-smi -q provides detailed GPU information, but it does not execute a full platform stress test. The Linux stress command can load CPU and I/O subsystems, but it is generic and does not validate the DGX platform using NVIDIA's health model. gpu_burn may stress GPUs, but it does not cover CPU, system memory, storage, and DGX-specific platform checks in the recommended way. During server bring-up, NVSM is preferred because it understands DGX hardware components and can identify platform health issues before the node is released to production workloads.
NEW QUESTION # 82
During multi-node HPL burn-in, GPUs show uneven utilization. Which configuration ensures balanced workload distribution?
Answer: B
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 # 83
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