2026 New NCP-AII Exam Questions Real NVIDIA Dumps [Q95-Q113]

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2026 New NCP-AII Exam Questions Real NVIDIA Dumps

Course 2026 NCP-AII Test Prep Training Practice Exam Download

NO.95 What command is needed to measure BER (Bit Error Rate)?

mlxconfig -d <device> q

ethtool -S <device>

mlxlink -d <device> -c -e

mstflint -d <device> q full

NO.96 You are tasked with troubleshooting a performance bottleneck in a multi-node, multi-GPU deep learning training job utilizing Horovod.
The training loss is decreasing, but the overall training time is significantly longer than expected. Which of the following monitoring approaches would provide the most insight into the cause of the bottleneck?

Using ‘nvidia-smi’ on each node to monitor GPU utilization and memory usage.

Enabling Horovod’s timeline and profiling features to visualize the communication patterns and identify synchronization bottlenecks.

Monitoring network bandwidth utilization on each node using ‘iftop’ or ‘iperf3’

Analyzing the training loss curve to identify potential issues with the model architecture or hyperparameters.

Using Shtop’ to monitor CPIJ utilization on each node.

NO.97 You are designing a storage solution for a new AI inference cluster that requires extremely low latency for model serving. Which storage technology and configuration would be MOST suitable to meet this stringent latency requirement?

A distributed file system deployed on spinning HDDs with a large read-ahead cache.

NVMe-oF (NVMe over Fabrics) using RDMA over Converged Ethernet (RoCE) connected to a cluster of NVMe drives.

A software-defined storage (SDS) solution running on commodity hardware with SATA SSDs.

Amazon S3 object storage accessed over a high-bandwidth internet connection.

A traditional Fibre Channel SAN with a dedicated storage array.

NO.98 Which of the following statements regarding the benefits of using a BlueField DPU for network offload are TRUE? (Select TWO)

Reduced CPU utilization on the host server for network-related tasks.

Simplified network configuration compared to traditional NICs.

Increased network throughput due to hardware acceleration.

Automatic compatibility with all existing network protocols without requiring software updates.

Elimination of the need for a dedicated network interface card (NIC).

NO.99 You have a server with two NVIDIA GPUs connected via NVLink. You want to verify that NVLink is functioning correctly. Which command(s) or tool(s) can you use to check the NVLink status and bandwidth?

‘nvidia-smi nvlink -statue

Ispci’

nvcc -version’

‘nvidia-smi topo -m’

‘nvidia-settings’ (GUI tool)

NO.100 Refer to the image:

Which MAC address should be used by the system administrator as the DPU’s out-of-band management interface MAC address?

0002C9270502

0002C9270500

0002C927050F

0002C927050E

NO.101 Which of the following tests should be used to check for the lowest possible latency between two nodes in a fabric?

ib_read_lat

ib_write_lat

ib_read_bw

iperf

NO.102 During multi-node HPL burn-in, GPUs show uneven utilization. Which configuration ensures balanced workload distribution?

Enable HPL_USE_NVSHMEM=1 for shared memory acceleration

HPL_RUN_GEMM_TESTS to skip validation

Set –gpu-affinity and –cpu-affinity to align GPU and NUMA nodes

HPL_OOC_TILE_M to 8192 for larger blocks

NO.103 A system administrator noticed a failure on a DGX H100 server. After a reboot, only the BMC is available. What could be the reason for this behavior?

There are more than two failed power supplies.

A boot disk has failed.

The network card has no link / connection.

Multiple GPUs have failed.

NO.104 You’re troubleshooting a DGX-I server exhibiting performance degradation during a large-scale distributed training job. ‘nvidia-smu shows all GPUs are detected, but one GPU consistently reports significantly lower utilization than the others. Attempts to reschedule orkloads to that GPU frequently result in CUDA errors. Which of the following is the MOST likely cause and the BEST initial roubleshooting step?

A driver issue affecting only one GPU; reinstall NVIDIA drivers completely.

A software bug in the training script utilizing that specific GPU’s resources inefficiently; debug the training script.

A hardware fault with the GPU, potentially thermal throttling or memory issues; run ‘nvidia-smi -i -q’ to check temperatures, power limits, and error counts.

Insufficient cooling in the server rack; verify adequate airflow and cooling capacity for the rack.

Power supply unit (PSU) overload, causing reduced power delivery to that GPU; monitor PSU load and check PSU specifications.

NO.105 A research organization deploys NVIDIA BlueField DPUs in an AI cluster to improve infrastructure efficiency. Shortly after deployment, administrators observe that CPU utilization on compute nodes decreases during storage and network-intensive workloads while GPU utilization remains unchanged. Which DPU capability most directly explains this improvement?

Executing CUDA kernels on behalf of GPUs

Offloading infrastructure services such as networking, storage, and security from the host CPU

Increasing GPU memory capacity through memory pooling

Replacing the InfiniBand fabric with PCIe communication

NO.106 You are setting up network fabric ports for hosts in an NVIDIA-Certified Professional A1 Infrastructure (NCP-AII) environment. You need to configure Jumbo Frames to improve network throughput. What is the typical MTU (Maximum Transmission Unit) size you would set on the network interfaces and switches, and why?

1500 bytes, as it’s the default and compatible with most networks.

9000 bytes, also known as Jumbo Frames, reduces overhead and improves throughput for large data transfers.

65535 bytes, the theoretical maximum MTU size, for maximum performance.

576 bytes, the minimum MTU size required by IPv4.

Any MTU size between 1500 and 9000 bytes; the specific value doesn’t matter.

NO.107 When configuring an out-of-core (OOC) HPL burn-in for a 40B matrix on 8x H100 nodes, which environment variable prevents GPU out-of-memory errors while reserving space for drivers?

export HPL_OOC_NUM_STREAMS=8

export HPL_OOC_MODE=0

export HPL_OOC_MAX_GPU_MEM=90

export HPL_OOC_SAFE_SIZE=4.0

NO.108 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.

Ensure that the InfiniBand or RoCE network is properly configured and that all servers can communicate with each other over the network. Verify the network interface names and IP addresses in the NCCL configuration.

Use the Slurm ‘srun’ command with the ‘-mpi=pmi2 option to launch the training job. This ensures that Slurm properly initializes the MPl environment and sets the NCCL environment variables.

Increase the ‘NCCL CONNECT TIMEOUT and *NCCL TIMEOUT environment variables to allow for longer network delays.

Disable the firewall on all servers to allow unrestricted network communication.

Decrease the batch size to reduce the amount of data transferred over the network.

NO.109 You are leading a project to enhance the energy efficiency of a data center that heavily relies on AI workloads. NVIDIA suggests moving beyond traditional metrics like Power Usage Effectiveness (PUE) to better capture the efficiency of modern data centers. Which strategy should you prioritize to develop more accurate energy-efficiency metrics?

Focus on integrating kilowatt-hours into existing metrics to better reflect the actual energy used for productive work.

Use watts-used as the primary measure of efficiency, as it accurately reflects the power input at any given time.

Use Power Usage Effectiveness as the primary metric while supplementing it with additional measures of useful work done per unit of energy.

Develop benchmarks tailored to specific workloads, such as MLPerf for AI applications, to better understand energy use in real-world scenarios.

NO.110 You are building a Docker image for a deep learning application that requires an NVIDIA GPU. Which of the following instructions is the most efficient way to ensure the NVIDIA drivers are available within the container, assuming you are using the nvidia/cuda/’ base image and want to minimize the image size?

COPY /usr/lib/nvidia /usr/local/nvidia/

RUN apt-get update && apt-get install -y nvidia-driver-470

FROM nvidia/cuda:ll .4.2-base-ubuntu20.04 AS builder RUN apt-get update && apt-get install -y -no-install-recommends software-properties-common RUN add-apt-repository ppa:graphics-drivers/ppa RUN apt-get update && apt-get install -y -no-install-recommends nvidia-driver-470 FROM ubuntu:20.04 COPY -from=builder /usr/lib/nvidia /usr/lib/ COPY -from=builder /usr/local/nvidia /usr/local/

FROM nvidia/cuda:ll .4.2-base-ubuntu20.04 RUN apt-get update && apt-get install -y -no-install-recommends nvidia-driver-470

Using ‘nvidia/cuda’ base image, the drivers are already included, so no further action is needed.

NO.111 A system administrator is working on a DGX A100 and needs to start a CUDA job. The job fails and a cudaErrorSystemNotReadyerror is returned. What is the cause of this error?

A GPU fails to register with the fabric.

FM is not completely done initializing.

There are too many running CUDA jobs.

CUDA is not properly installed.

NO.112 A network engineer is tasked with configuring the management, storage, and compute networks for a new DGX BasePOD deployment. Which statement best describes the network segmentation required for optimal operation?

A single VLAN for all types of network traffic.

Two networks: one for management and one for compute.

Four networks: compute, storage, out-of-band, and management.

NO.113 Consider the following ‘ibroute’ command used on an InfiniBand host: ‘ibroute add dest Oxla dev ib0’. What is the MOST likely purpose of this command?

To add a default route for all traffic destined outside the InfiniBand subnet.

To create a static route for traffic destined to LID Ox1a, using the InfiniBand interface ib0.

To configure the MTU size on the ib0 interface to Ox1a bytes.

To disable routing on the ib0 interface.

To configure a static route for traffic destined to IP address Ox1a, using the InfiniBand interface ib0.

NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 2	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.
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	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 5	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.