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NVIDIA NCP-AII Exam Overview:

Certification Vendor:	NVIDIA
Exam Name:	NVIDIA Certified Professional – AI Infrastructure
Exam Number:	NCP-AII
Exam Duration:	90 minutes
Exam Price:	$195 USD
Real Exam Qty:	50
Certificate Validity Period:	2 years
Passing Score:	700 (scale of 0-1000)
Exam Format:	Multiple Select, Multiple Choice
Available Languages:	English
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 시험요강:

주제	소개
주제 1	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.
주제 2	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.
주제 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.
주제 4	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
주제 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.

최신 NVIDIA-Certified Professional NCP-AII 무료샘플문제 (Q194-Q199):

질문 # 194
An engineer needs to validate NVLink Switch functionality on a DGX H100 system with 8 GPUs. Which NCCL command verifies intra-node NVLink bandwidth?

A. all_reduce_perf -b 8 -e 16G -f 2 -g 4 with NCCL_TESTS_SPLIT="MOD 2"
B. broadcast_perf -b 8 -e 16G -f 2 -g 8 without split configuration
C. all_reduce_perf -b 8 -e 16G -f 2 -g 8 with NCCL_TESTS_SPLIT="OR 0x7"
D. all_reduce_perf -b 8 -e 16G -f 2 -g 1 repeated 8 times

정답：C

설명：
The NVIDIA Collective Communications Library (NCCL) "Tests" are used to verify the maximum achievable bandwidth of the interconnects. On a DGX H100, the GPUs are connected via a dedicated high- bandwidthNVLink Switch fabric(NVLink 4), which provides significantly higher throughput than PCIe. To validate the intra-node (within a single server) performance, the all_reduce_perf test is used. The command in Option D is specifically designed to stress all 8 GPUs (-g 8) across a wide range of message sizes (8 bytes to
16G). The use of the environment variable NCCL_TESTS_SPLIT with the bitwise "OR" or "AND" masks allows the engineer to isolate specific traffic patterns or groups of GPUs to ensure the NVLink switches are distributing the load evenly. For a standard 8-GPU H100 tray, achieving a "Bus Bandwidth" of ~450 GB/s to
900 GB/s (depending on the precision and message size) confirms that the NVLink fabric is operating at its theoretical peak. Using only 4 GPUs (Option B) or 1 GPU (Option C) would not provide a complete picture of the NVLink switch bisection bandwidth.

질문 # 195
You're debugging performance issues in a distributed training job. 'nvidia-smi' shows consistently high GPU utilization across all nodes, but the training speed isn't increasing linearly with the number of GPUs. Network bandwidth is sufficient. What is the most likely bottleneck?

A. Inefficient data loading and preprocessing pipeline, causing GPUs to wait for data.
B. NCCL is not configured optimally for the network topology, leading to high communication overhead.
C. CUDA Graphs is not being utilized.
D. The learning rate is not adjusted appropriately for the increased batch size across multiple GPUs.
E. The global batch size has exceeded the optimal point for the model, reducing per-sample accuracy and slowing convergence.

정답：A,B,D,E

설명：
If GPUs are highly utilized but scaling is poor, the bottleneck is likely not GPU compute itself. Inefficient data pipelines mean GPUs spend time idle waiting for data. Suboptimal NCCL configurations result in communication overhead negating the benefit of more GPUs. Incorrect learning rate with larger batch size will impact covergence. Batch sizes can affect convergence and model effectiveness. While CUDA Graphs improves performance, the other answers are more pertinent to the question.

질문 # 196
An enterprise builds a Kubernetes-based AI platform using NVIDIA GPU Operator. After adding several new GPU nodes, administrators notice that NVIDIA drivers, container runtime components, and monitoring agents are installed automatically without manual intervention.
Which capability of GPU Operator enables this behavior?

A. Dynamic migration of virtual machines between GPU nodes
B. Automatic firmware flashing of GPUs
C. Automatic BIOS configuration for every worker node
D. Lifecycle management of GPU software components through Kubernetes

정답：D

설명：
GPU Operator simplifies Kubernetes deployments by managing GPU drivers, the NVIDIA Container Toolkit, device plugins, DCGM exporters, and related software as Kubernetes resources. This automates installation, upgrades, and lifecycle management across the cluster. It does not configure BIOS settings or perform virtual machine migration.

질문 # 197
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?

A. Tape storage for its cost-effectiveness and archival capabilities
B. File storage for hierarchical organization and easy navigation
C. Block storage for low latency and high performance
D. Object storage for scalability and metadata management

정답：D

설명：
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.

질문 # 198
During BCM cluster setup, an engineer must configure bonded network interfaces on DGX nodes for high availability. Which cmsh command sequence properly configures a bond0 interface with two physical NICs?

정답：A

설명：
In BCM cmsh, a bonded interface is created by adding a bond interface, appending the two physical NICs as bond members, setting the bonding mode, and assigning the bond to the appropriate network. This configures bond0 with the two physical interfaces for high availability on the internal network.

질문 # 199
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