NCP-AII Dumps (V10.03) Ensure Your 2026 NVIDIA Certified Professional AI Infrastructure Exam Preparation – NCP-AII Free Dumps (Part 1, Q1-Q39) Are Online

1. After replacing a GPU in a multi-GPU server, you notice that the new GPU is consistently running at a lower clock speed than the other GPUs, even under load. *nvidia-smi’ shows the ‘Pwr’ state as ‘P8’ for the new GPU, while the others are at ‘PO’.

What is the MOST probable cause?

2. You are experiencing link flapping (frequent up/down transitions) on several InfiniBand links in your AI infrastructure. This is causing intermittent connectivity issues and performance degradation.

What are the MOST likely causes of this issue, and what steps should you take to troubleshoot and resolve it? (Select TWO)

3. A GPU in your AI server consistently overheats during inference workloads. You’ve ruled out inadequate cooling and software bugs.

Running ‘nvidia-smi’ shows high power draw even when idle.

Which of the following hardware issues are the most likely causes?

4. Your AI training pipeline involves a pre-processing step that reads data from a large HDF5 file. You notice significant delays during this step. You suspect the HDF5 file structure might be contributing to the slow read times.

What optimization technique is MOST likely to improve read performance from this HDF5 file?

5. You have a server equipped with multiple NVIDIA GPUs connected via NVLink. You want to monitor the NVLink bandwidth utilization in real-time.

Which tool or method is the most appropriate and accurate for this?

6. Consider a scenario where you are setting up a high-performance computing cluster with several GPU-accelerated nodes using Slurm as the resource manager. You want to ensure that jobs requesting GPUs are only scheduled on nodes with the appropriate NVIDIA drivers and CUDA toolkit installed.

How can you achieve this within Slurm?

7. After replacing a faulty NVIDIA GPU, the system boots, and ‘nvidia-smi’ detects the new card. However, when you run a CUDA program, it fails with the error "‘no CUDA-capable device is detected’". You’ve confirmed the correct drivers are installed and the GPU is properly seated.

What’s the most probable cause of this issue?

8. You are tasked with configuring an NVIDIA NVLink� Switch system. After physically connecting the GPUs and the switch, what is the typical first step in the software configuration process?

9. You are managing a server farm of GPU servers used for A1 model training. You observe frequent GPU failures across different servers.

Analysis reveals that the failures often occur during periods of peak ambient temperature in the data center. You can’t immediately improve the data center cooling.

What are TWO proactive measures you can implement to mitigate these failures without significantly impacting training performance?

10. You are running a distributed training job across multiple nodes, using a shared file system for storing training data. You observe that some nodes are consistently slower than others in reading data.

Which of the following could be contributing factors to this performance discrepancy? Select all that apply.

11. You are configuring an InfiniBand subnet with multiple switches. You need to ensure that traffic between two specific nodes always takes the shortest path, bypassing a potentially congested link.

Which of the following approaches is MOST effective for achieving this using InfiniBand’s routing capabilities?

12. Given the following ‘nvswitch-cli’ output, what does the ‘Link Speed’ indicate, and what potential bottleneck might a low ‘Link Speed’ suggest?

13. You are configuring a server with multiple GPUs for CUDA-aware MPI.

Which environment variable is critical for ensuring proper GPU affinity, so that each MPI process uses the correct GPU?

14. You are running a large-scale distributed training job on a cluster of AMD EPYC servers, each equipped with multiple NVIDIAA100 GPUs. You are using Slurm for job scheduling. The training process often fails with NCCL errors related to network connectivity.

What steps can you take to improve the reliability of the network communication for NCCL in this environment? Choose the MOST appropriate answers.

15. You are using NVIDIA Spectrum-X switches in your A1 infrastructure. You observe high latency between two GPU servers during a large distributed training job. After analyzing the switch telemetry, you suspect a suboptimal routing path is contributing to the problem.

Which of the following methods offers the MOST granular control for influencing traffic flow within the Spectrum-X fabric to mitigate this?

16. A data center is designed for A1 training with a high degree of east-west traffic. Considering cost and performance, which network topology is generally the most suitable?

17. Which of the following are key considerations when choosing between CPU pinning and NUMA (Non-Uniform Memory Access) awareness for a distributed training job on a multi-socket AMD EPYC server with multiple GPUs?

18. You are implementing a distributed deep learning training setup using multiple servers connected via NVLink switches. You want to ensure optimal utilization of the NVLink interconnect.

Which of the following strategies would be MOST effective in achieving this goal?

19. You are deploying a multi-tenant AI infrastructure where different users or groups have isolated network environments using VXLAN.

Which of the following is the MOST important consideration when configuring the VTEPs (VXLAN Tunnel Endpoints) on the hosts to ensure proper network isolation and performance?

20. You are configuring a network for a distributed training job using multiple DGX servers connected via InfiniBand. After launching the training job, you observe that the inter-GPU communication is significantly slower than expected, even though ‘ibstat’ shows all links are up and active.

What is the MOST likely cause of this performance bottleneck?

21. Consider a scenario where you are running a CUDA application on an NVIDIA GPU. The application compiles successfully but crashes during runtime with a *CUDA ERROR ILLEGAL ADDRESS* error. You’ve carefully reviewed your code and can’t find any obvious out- of-bounds memory accesses.

What advanced debugging techniques could help you pinpoint the source of this error?

22. You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. ‘nvidia-smi’ reports a persistent "XID 13" error for that GPU.

What is the most likely cause?

23. After upgrading the network card drivers on your A1 inference server, you experience intermittent network connectivity issues, including packet loss and high latency. You’ve verified that the physical connections are secure.

Which of the following steps would be most effective in troubleshooting this issue?

24. You are deploying a new A1 inference service using Triton Inference Server on a multi-GPU system. After deploying the models, you observe that only one GPU is being utilized, even though the models are configured to use multiple GPUs.

What could be the possible causes for this?

25. Consider a scenario where you’re using GPUDirect Storage to enable direct memory access between GPUs and NVMe drives. You observe that while GPUDirect Storage is enabled, you’re not seeing the expected performance gains.

What are potential reasons and configurations you should check to ensure optimal GPUDirect Storage performance? Select all that apply.

26. You are deploying a multi-tenant A1 infrastructure with strict isolation requirements.

Which network technology would be most suitable for creating isolated virtual networks for each tenant?

27. You are configuring a switch port connected to a host in an NCP-AII environment. The host is running RoCEv2.

To optimize performance and prevent packet loss, which flow control mechanism should you enable on the switch port?

28. You’re optimizing a deep learning model for deployment on NVIDIA Tensor Cores. The model uses a mix of FP32 and FP16 precision. During profiling with NVIDIA Nsight Systems, you observe that the Tensor Cores are underutilized.

Which of the following strategies would MOST effectively improve Tensor Core utilization?

29. What is the role of GPUDirect RDMA in an NVLink Switch-based system, and how does it improve performance?

30. You’re profiling the performance of a PyTorch model running on an AMD server with multiple NVIDIA GPUs. You notice significant overhead in the data loading pipeline.

Which of the following strategies can help optimize data loading and improve GPU utilization? Select all that apply.

31. You are designing a network for a distributed training job utilizing multiple GPUs across multiple nodes.

Which network characteristic is MOST critical for minimizing training time?

32. In a large-scale InfiniBand fabric, you need to implement a mechanism to prioritize traffic for a specific application that requires low latency and high bandwidth. You want to leverage Quality of Service (QOS) to achieve this.

Which of the following steps are essential to properly configure QOS in this scenario? (Select THREE)

33. You’re optimizing an AMD EPYC server with 4 NVIDIAAIOO GPUs for a large language model training workload. You observe that the GPUs are consistently underutilized (50-60% utilization) while the CPUs are nearly maxed out.

Which of the following is the MOST likely bottleneck?

34. You are troubleshooting performance issues in an A1 training clusten You suspect network congestion.

Which of the following network monitoring tools would be MOST helpful in identifying the source of the congestion?

35. You are using GPU Direct RDMA to enable fast data transfer between GPUs across multiple servers. You are experiencing performance degradation and suspect RDMA is not working correctly.

How can you verify that GPU Direct RDMA is properly enabled and functioning?

36. When setting up a multi-server, multi-GPU environment using NVLink switches, what is the primary consideration when planning the network topology for optimal performance?

37. A large A1 model is training using a dataset stored on a network-attached storage (NAS) device. The data transfer speeds are significantly lower than expected. After initial troubleshooting, you discover that the MTU (Maximum Transmission Unit) size on the network interfaces of the training server and the NAS device are mismatched. The server is configured with an MTIJ of 1500, while the NAS device is configured with an MTU of 9000 (Jumbo Frames).

What is the MOST likely consequence of this MTU mismatch, and what action should you take?

38. You are setting up a virtualized environment (using VMware vSphere) to run GPU-accelerated workloads. You have multiple physical GPUs in your server and want to assign specific GPUs to different virtual machines (VMs) for dedicated access.

Which vSphere technology would BEST support this?

39. Consider the following *iptables’ rule used in an A1 inference server.

What is its primary function?

iptables -A INPUT -p tcp --dport 8080 -j ACCEPT