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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 |
| Exam Price: | $400 |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 70-75 |
| Related Certifications: | NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) |
| 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 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 # 79
An NVIDIA DGX server with 8 GPUs is experiencing performance issues during a distributed deep learning training run. You suspect a problem with the GPU interconnects. You have already confirmed that NVLink is active. What is the most thorough approach to diagnose potential bandwidth or latency bottlenecks in the GPU-to-GPlJ communication paths?
Answer: A
Explanation:
Explanation:Diagnosing GPU interconnect bottlenecks requires a multi-faceted approach. Visualizing the topology with 'nvidia-smi topo -m' , running NCCL benchmarks, monitoring GPU utilization for imbalances, and checking system logs for errors are all necessary for identifying and resolving communication issues. Ignoring any of these steps could lead to incomplete diagnosis and suboptimal performance.
NEW QUESTION # 80
An InfiniBand administrator needs to run performance benchmarks on new devices added to the fabric. What tool should be used to check the latency?
Answer: A
Explanation:
While the InfiniBand fabric is known for high bandwidth, its defining characteristic for AI workloads is ultra- low, sub-microsecond latency. When new nodes or switches are added, administrators must verify that the point-to-point latency meets the hardware specifications. The ib_write_lat utility is the standard micro- benchmark from the perftest suite used for this purpose. It measures the time it takes to complete an RDMA Write operation between two nodes. This tool is "verified" because it operates directly over the InfiniBand Verbs layer, bypassing the CPU overhead of the standard TCP/IP stack. Unlike tcpdump (Option A), which is used for packet capture, or ibdiagnet (Option C), which is used for fabric-wide discovery and error reporting, ib_write_lat provides a granular, nanosecond-level measurement of the link's responsiveness. In an AI cluster, even a small increase in latency can cause a "straggler" effect in distributed training, where all GPUs wait for the slowest link to complete a synchronization step.
NEW QUESTION # 81
A customer has just completed the first boot of their DGX system and is prompted to create an administrative user. What is the correct approach for setting up this user to ensure secure BMC and GRUB access?
Answer: B
Explanation:
During initial DGX setup, the administrative user should be created with unique, strong credentials because it is used for secure management access, including BMC and GRUB-related authentication. Avoiding default or weak credentials reduces the risk of unauthorized system control.
NEW QUESTION # 82
After a firmware upgrade on a DGX H100, the administrator notices that one GPU is not detected by the system. Which troubleshooting step should be performed first to identify the root cause?
Answer: A
Explanation:
The first step is to review the firmware update logs and run nvsm show health. After a DGX H100 firmware upgrade, a missing GPU can result from incomplete firmware activation, failed component update, PCIe enumeration failure, GPU tray communication issues, BMC inventory mismatch, or an actual hardware fault.
NVSM is the correct DGX platform-level health tool because it checks hardware state across GPUs, NVSwitch components, PCIe devices, storage, power, cooling, and system sensors. Firmware logs are equally important because they show whether each update completed successfully and whether a reboot, cold power cycle, BMC reset, or AC power cycle is still required. Replacing the GPU immediately is premature and may cause unnecessary downtime. Ignoring the issue is unsafe because production AI workloads expect all GPUs to be visible and healthy. Re-running firmware across all components without diagnosis can hide the original failure or introduce more risk. Proper bring-up practice is to collect evidence, verify hardware health, confirm firmware activation state, and then decide whether reseating, power cycling, reapplying firmware, or service escalation is required.
NEW QUESTION # 83
A team is validating a DGX BasePOD deployment. Using cmsh, they run a command to check GPU health across all nodes. What indicates that the system is ready for AI workloads?
Answer: C
Explanation:
In an NVIDIA DGX BasePOD or SuperPOD environment, "Cluster Health" is a binary state: either the entire fabric and all compute resources are ready, or the cluster is considered degraded. Using the Bright Cluster Manager (BCM) shell (cmsh), administrators can aggregate telemetry from every node in the cluster. For a system to be considered "Production Ready," every single GPU across the multi-node deployment must report a status of Health = OK. This verification ensures that the hardware is communicating correctly over the PCIe bus, the NVLink fabric is initialized, and no ECC (Error Correction Code) memory errors are present. If even a single GPU in a 32-node cluster is unhealthy, collective communication libraries like NCCL may hang or experience significant performance penalties during "All-Reduce" operations, as the entire job typically scales to the speed of the slowest/unhealthiest component. Therefore, seeing Status_Health = OK for every device is the mandatory exit criterion for the bring-up phase.
NEW QUESTION # 84
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