NVIDIA NCP-AII Questions To Gain Brilliant Result [2026]

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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.

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NVIDIA AI Infrastructure Sample Questions (Q52-Q57):

NEW QUESTION # 52
A distributed training job using multiple nodes, each with eight NVIDIA GPUs, experiences significant performance degradation. You notice that the network bandwidth between nodes is consistently near its maximum capacity. However, 'nvidia-smi' shows low GPU utilization on some nodes. What is the MOST likely cause?

A. The CPU is heavily loaded, causing contention for network resources.
B. Data is not being distributed evenly across the nodes; some nodes are waiting for data from others.
C. The GPUs are overheating, causing thermal throttling.
D. The NVIDIA drivers are outdated, causing communication bottlenecks.
E. The network interface cards (NICs) are faulty, causing packet loss and retransmissions.

Answer: B

Explanation:
High network bandwidth utilization combined with low GPU utilization on some nodes strongly suggests a data imbalance. Some nodes are likely waiting for data from other nodes, causing them to be idle while the network is saturated. This is a common problem in distributed training and requires addressing the data distribution strategy. While other factors (overheating, outdated drivers, faulty NICs, CPU load) could contribute, they are less likely to be the primary cause given the observed symptoms.

NEW QUESTION # 53
After initial setup and health checks, the DGX H100 system administrator wants to verify that containers can access GPUs before running production workloads. Which method is recommended for this validation?

A. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi
B. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 systemctl
C. sudo docker run --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi
D. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 ls-la

Answer: A

Explanation:
Running a CUDA container with --gpus all and executing nvidia-smi is the standard validation that Docker, the NVIDIA container runtime, drivers, and GPU access are working correctly before production workloads are launched.

NEW QUESTION # 54
Which of the following are crucial considerations when validating the hardware operation of an NVIDIA-Certified Professional AI Infrastructure server before deploying a production A1 workload? (Select all that apply)

A. Checking if the server's case fans are spinning.
B. Verifying that the server's power supply units (PSUs) are operating within their specified voltage and current ranges under full load.
C. Only testing the GPUs with a synthetic benchmark like 'linpack'.
D. Ensuring the BIOS and BMC firmware are up to date.
E. Confirming the correct installation and functionality of all network interfaces (e.g., Ethernet, InfiniBand) relevant to the workload.

Answer: B,D,E

Explanation:
Validating hardware operation involves checking BIOS/BMC firmware for compatibility and security updates, ensuring PSUs are within operating parameters, and verifying network interface functionality. Synthetic benchmarks alone aren't sufficient; the fans spinning is a basic check, but doesn't validate actual cooling performance under load. Complete hardware validation involves a broad range of checks to prevent later issues.

NEW QUESTION # 55
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?

A. Using the same VNI for all tenants to maximize network utilization.
B. Disabling multicast routing to prevent broadcast traffic.
C. Using the default MTU size of 1500 bytes for VXLAN traffic.
D. Ensuring that each tenant has a unique VXLAN Network Identifier (VNI) to isolate their traffic.
E. Using the same IP address for all VTEPs to simplify routing.

Answer: D

Explanation:
The most critical aspect of VXLAN configuration for multi-tenancy is ensuring that each tenant has a unique VNI. The VNI is a 24-bit identifier that segments the VXLAN network into isolated logical networks. Using unique VNIs guarantees that traffic from one tenant remains isolated from other tenants, preventing data leakage and ensuring security. Using overlapping or shared VNIs would defeat the purpose of VXLAN- based isolation.

NEW QUESTION # 56
A system administrator needs to validate a GPU-based server and ensure that no errors occur under load. What command should be used?

A. nvsm dump health
B. nvsm stress-test
C. stress-test --usage
D. nvsm show health

Answer: B

Explanation:
nvsm stress-test is used to place the DGX/GPU-based server under load and validate that the system remains stable without reporting hardware or GPU errors during the test.

NEW QUESTION # 57
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