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NVIDIA NCP-AII Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA Certified Professional โ€“ AI Infrastructure
Exam Number:NCP-AII
Exam Price:$195 USD
Available Languages:English
Real Exam Qty:50
Passing Score:700 (scale of 0-1000)
Certificate Validity Period:2 years
Exam Format:Multiple Select, Multiple Choice
Exam Duration:90 minutes
Related Certifications:NCP-AI
NCP-DES
Sample Questions:NVIDIA NCP-AII Sample Questions
Exam Way:Online proctored exam (Pearson VUE)
Pre Condition:Recommended: hands-on experience with NVIDIA AI infrastructure products; basic knowledge of Linux, networking, and data center operations
Official Syllabus URL:https://www.nvidia.com/en-us/certifications/ncp-ai-infra/

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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
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
  • 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 4
  • 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 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.

NVIDIA AI Infrastructure Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which of the following tests should be used to check for the lowest possible latency between two nodes in a fabric?

Answer: A

Explanation:
The ib_write_lat test is used to check low-level RDMA write latency between two nodes in an InfiniBand fabric. In NVIDIA AI infrastructure, latency validation is important because distributed training workloads depend on fast GPU-to-GPU synchronization across servers. Operations such as all-reduce, reduce-scatter, and parameter synchronization are sensitive to delay, especially as the number of nodes increases. The ib_write_lat utility is part of the RDMA perftest toolset and is commonly used during fabric validation to confirm that adapters, switches, cabling, routing, firmware, and Subnet Manager configuration are working correctly. ib_read_bw measures RDMA read bandwidth, not latency. ib_write_bw measures RDMA write bandwidth, not the lowest latency behavior. ib_read_lat measures read latency, but write latency is commonly used as a practical low-level latency check for RDMA fabric health. Consistently low and stable ib_write_lat results help confirm that the fabric is ready for higher-level validation with NCCL, HPL, and distributed AI workload tests.


NEW QUESTION # 37
An administrator needs to manually deploy the BlueField image on a target DPU. The administrator downloads the new image file and needs to flash it to the hardware. Which command should the administrator use?

Answer: B

Explanation:
The correct command is bfb-install --rshim, normally used with the BFB image path and the appropriate RShim device, such as sudo bfb-install --rshim rshim0 --bfb < image_path.bfb > . BlueField software images are commonly deployed as BFB files, and RShim provides the host-side path used to push the image to the BlueField device. NVIDIA documentation states that the bfb-install utility is included with the RShim package and is used to push the BFB image to the BlueField side while reporting installation progress. The mlnx_fw_updater.pl tool is for firmware updates, not full BlueField OS image deployment. apt install doca- runtime installs DOCA runtime packages but does not flash a BlueField image. Using dd directly to a made- up device path is unsafe and not the supported method for deploying a BlueField boot stream image. During bring-up, using the supported BFB installation workflow helps ensure the DPU boots a valid signed image and enters a known operational state.


NEW QUESTION # 38
A media company is developing an AI platform for video content analysis that requires storing and processing large volumes of unstructured video data. The platform must support high throughput for data ingestion and provide efficient access for real-time analytics. Given these requirements, which storage strategy should the company implement?

Answer: A

Explanation:
Object storage is best suited for large volumes of unstructured video data because it scales efficiently, supports high-throughput ingestion, and uses rich metadata to organize and retrieve content for analytics workflows. This makes it a strong fit for AI-based video analysis and real- time data access patterns.


NEW QUESTION # 39
Consider the following Dockerfile snippet:

This Dockerfile is used to build a deep learning application. After building and running a container from this image, you observe that the application is not detecting the GPU. You have verified that the NVIDIA Container Toolkit is installed and configured correctly on the host. What is the most likely reason for this issue?

Answer: E

Explanation:
The 'docker run' command must include the =gpus all' (or equivalent) flag to explicitly request GPU resources for the container (C). The base image 'nvidia/cuda:ll .6.2-base-ubuntu20.04' provides the CUDA runtime, but the NVIDIA Container Toolkit on the host handles the GPU device mapping. The application code (B) doesn't need to explicitly request GPUs; the CUDA runtime will handle that. 'nvidia-pyindex' (D) is related to package management, not GPU detection. The Dockerfile includes a specific CUDA version that mitigates version differences (E). The base image does not include the Container Toolkit, which is installed on the HOST.


NEW QUESTION # 40
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?

Answer: C

Explanation:
The DGX Station RAID configuration script supports configuring the data array as RAID 0 or RAID 5. RAID 0 provides maximum performance and capacity, while RAID 5 provides fault tolerance with usable capacity tradeoff.


NEW QUESTION # 41
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