Use Free NCP-AII Exam Questions that Stimulates Actual EXAM [Q100-Q119]

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Use Free NCP-AII Exam Questions that Stimulates Actual EXAM

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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
Topic 2	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3	System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC OOB TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Topic 4	Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm Enroot Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.
Topic 5	Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD Intel servers and storage.

NEW QUESTION 100
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?

CUDA VISIBLE DEVICES

CUDA DEVICE ORDER

LD LIBRARY PATH

MPI GPU SUPPORT

CUDA LAUNCH BLOCKING-I

NEW QUESTION 101
A system administrator needs to validate a GPU-based server and ensure that no errors occur under load. What command should be used?

stress-test –usage

nvsm stress-test

nvsm dump health

nvsm show health

NEW QUESTION 102
A critical AI model training job consistently fails on a specific GPU server in your cluster after running for approximately 24 hours.
Monitoring data shows a sudden drop in GPU power consumption followed by a system reboot. All other GPUs on the server appear normal. The server has redundant PSUs. What is the MOST likely cause?

A software bug in the A1 model causing a kernel panic specifically triggered after 24 hours of execution.

Thermal runaway on the GPU due to a failing thermal interface material (TIM) between the GPU die and the heatsink.

A transient power supply issue affecting only one of the redundant PSUs, triggering a system-wide protection mechanism.

ECC memory errors accumulating over time, eventually leading to a non-recoverable system fault.

A driver crash, causing the GPU to reset and the system to reboot.

NEW QUESTION 103
Your company is planning to expand its AI capabilities significantly over the next five years. To future-proof your storage infrastructure, you need a solution that can scale in both capacity and performance. Which of the following strategies best ensures that your storage infrastructure remains adaptable to future AI demands?

Deploy an all-flash array and remove data tiering to reduce latency.

Implement single-tier cloud storage solution to leverage cloud scalability.

Use a hybrid cloud model combining scalable cloud resources with on-premises infrastructure.

Implement on-premises block storage system with periodic hardware upgrades.

NEW QUESTION 104
Why is it important to provide a large and high-performance local cache (using SSDs configured as RAID-0) for deep learning workloads on DGX systems?

Using local SSD cache in RAID-0 enables direct GPU access to files without host CPU involvement, further boosting performance.

Local SSD cache allows users to increase the number of NFS threads on the server without impacting storage reliability.

A local SSD cache in RAID-0 ensures that most training data is read only once from the network, significantly reducing NFS traffic.

Local SSD cache in RAID-0 is necessary to provide redundancy in case one of the drives fails during long training runs.

NEW QUESTION 105
After running a 24-hour stress test on a DGX node, the administrator should verify which two key metrics to ensure system stability?

Average CPU usage > 80% and Docker container uptime.

No thermal throttling events and consistent GPU utilization > 95% throughout the test.

SSD write endurance and RAM capacity.

Total energy consumption and NVLink bandwidth.

NEW QUESTION 106
Your deep learning training job that utilizes NCCL (NVIDIA Collective Communications Library) for multi-GPU communication is failing with “NCCL internal error, unhandled system error” after a recent CUDA update. The error occurs during the ‘all reduce’ operation.
What is the most likely root cause and how would you address it?

Incompatible NCCL version with the new CUDA version. Update NCCL to a version compatible with the installed CUDA version.

Insufficient shared memory allocated to the CUDA context. Increase the shared memory limit using ‘cudaDeviceSetLimit(cudaLimitSharedMemory, new_limity.

Firewall rules blocking inter-GPU communication. Configure the firewall to allow communication on the NCCL-defined ports (typically 8000-8010).

Faulty network cables used for inter-node communication (if the training job spans multiple servers). Replace the network cables with certified high-speed cables.

GPU Direct RDMA is not properly configured. Check ‘dmesg’ for errors and ensure RDMA is enabled.

NEW QUESTION 107
A system administrator needs to change the RAID level of DGX station and use the script included in the DGX software with no data on the array. What are the options the administrator can choose?

RAID 0 and RAID 5

RAID 1 and RAID 5

RAID 0 and RAID 1

RAID 10 and RAID 5

NEW QUESTION 108
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?

Roll back the network card drivers to the previous version.

Check the system logs for error messages related to the network card or driver.

Run network diagnostic tools like ‘ping’, ‘traceroute’, and ‘iperf3’ to assess the network performance.

Reinstall the operating system.

Update the server’s BIOS.

NEW QUESTION 109
A system administrator wants to determine if ConnectX ports are configured in Ethernet or InfiniBand modes. What command should be used?

ibstat

netstat

iostat

vmstat

NEW QUESTION 110
During East-West fabric validation on a 64-GPU cluster, an engineer runs all_reduce_perf and observes an algorithm bandwidth of 350 GB/s and bus bandwidth of 656 GB/s. What does this indicate about the fabric performance?

Inconclusive; rerun with point-to-point tests.

Optimal performance; bus bandwidth near theoretical peak for NDR InfiniBand.

Critical failure; bus bandwidth exceeds hardware capabilities.

Suboptimal performance; algorithm bandwidth should match bus bandwidth.

NEW QUESTION 111
After running a 24-hour stress test on a DGX node, the administrator should verify which two key metrics to ensure system stability?

Total energy consumption and NVLink bandwidth.

SSD write endurance and RAM capacity.

Average CPU usage ≥80% and Docker container uptime.

No thermal throttling events and consistent GPU utilization ≥95% throughout the test.

NEW QUESTION 112
You are setting up a multi-GPU AI server for deep learning. You want to ensure optimal inter-GPU communication. Which of the following interconnect technologies would provide the BEST performance?

PCle Gen3 x16

PCle Gen4 x16

NVLink

Ethernet

Infiniband

NEW QUESTION 113
After upgrading NVIDIA GPU Operator, Kubernetes reports all worker nodes as Ready, but newly created GPU workloads remain in the Pending state. The administrator confirms that GPUs are healthy and visible to the operating system. Which troubleshooting step should be performed first?

Verify that the NVIDIA Device Plugin is running successfully on GPU nodes.

Replace every GPU driver with an earlier version immediately.

Increase CPU requests for every application pod.

Disable the Kubernetes scheduler and assign pods manually.

NEW QUESTION 114
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?

Ispci -v to verify the adapter is detected and its resources are allocated correctly.

ibstat to check the adapter’s status, link speed, and active ports.

ethtool to examine the adapter’s Ethernet settings and statistics.

ping to test basic network connectivity.

nvsmimonitord to monitor GPU metrics and detect anomalies.

NEW QUESTION 115
For a 48-hour NCCL burn-in test, which parameters ensure sustained fabric stress while detecting silent data corruption?

broadcast_perf -b 4G -e 16G -w 160

all_reduce_perf -b 8G -e 32G -c 1000 -z 1 -G 1000

all_reduce_perf -b 8G -e 32G -z 1 -G 1000

reduce_scatter_perf -f 2 -g 8

NEW QUESTION 116
An enterprise decides to build a high-performance Ethernet-based AI cluster instead of using InfiniBand. To achieve RDMA functionality with minimal CPU overhead, which technology should the network architect implement?

VXLAN

RoCE

MPLS

GRE

NEW QUESTION 117
You are tasked with creating a custom Docker image for a deep learning application that requires a specific version of cuDNN. You want to minimize the image size while ensuring that the cuDNN libraries are correctly installed and configured. What is the most efficient way to achieve this?

Download the cuDNN archive from NVIDIA, extract the libraries, and manually copy them to the appropriate locations within the Dockerfile.

Use a multi-stage Docker build, using a base image with the desired CUDA version for building and then copying only the necessary cuDNN libraries to a smaller runtime image.

Install the entire CUDA toolkit within the Docker image, even if only cuDNN is needed.

Use the NVIDIA Container Toolkit to dynamically inject the cuDNN libraries into the container at runtime.

Use a pre-built CUDA base image and install cuDNN during the container run.

NEW QUESTION 118
You’ve flashed the BlueField OS to your SmartNlC, but you need to customize the kernel command line arguments (bootargs) to enable a specific feature. Where is the MOST appropriate place to modify these arguments for persistent changes that survive reboots?

In the bootloader configuration file (e.g., extlinux.conf or grub.cfg) on the BlueFieId’s flash memory.

In the ‘/etc/default/grub’ file on the BlueField OS, followed by updating the GRUB configuration.

Directly in the kernel image file itself using a hex editor.

Passing it as an argument to bfboot during deployment.

In the ‘/proc/cmdline’ file. This allows immediate changes.

NEW QUESTION 119
A system administrator needs to troubleshoot multiple GPUs in a cluster environment. Which would be the most efficient tool for this?

NVIDIA-SMI

GPU Health Analyzer

VMware Aria Operations

NVIDIA-DCGM

BEST Verified NVIDIA NCP-AII Exam Questions (2026) : https://www.testbraindump.com/NCP-AII-exam-prep.html

Use Free NCP-AII Exam Questions that Stimulates Actual EXAM [Q100-Q119]

NVIDIA NCP-AII Exam Syllabus Topics:

NCA-GENL Premium Files Updated Sep-2026 Practice Valid Exam Dumps Question [Q38-Q62]

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