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NVIDIA NCP-AII Exam Overview:

Certification Vendor:	NVIDIA
Exam Name:	NVIDIA AI Infrastructure (NCP-AII) Certification Exam
Exam Number:	NCP-AII
Available Languages:	English
Related Certifications:	NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Professional AI Operations (NCP-AIO)
Real Exam Qty:	70-75
Exam Price:	$400
Certificate Validity Period:	2 years
Exam Format:	Multiple-choice, Scenario-based questions
Exam Duration:	120 minutes
Recommended Training:	AI Infrastructure & Operations Fundamentals (NVIDIA Training) AI Infrastructure Professional Workshop
Exam Registration:	Official NVIDIA Certification Portal NVIDIA AI Infrastructure Certification Page
Sample Questions:	NVIDIA NCP-AII Sample Questions
Exam Way:	Online proctored exam (remote) or authorized test center depending on region
Pre Condition:	Recommended 2–3 years of experience working in data center environments with NVIDIA hardware solutions (GPU servers, networking, storage).
Official Syllabus URL:	https://www.nvidia.com/en-eu/learn/certification/ai-infrastructure-professional/

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NVIDIA NCP-AII復習過去問、NCP-AII日本語復習赤本

効果的な勤勉さが結果に正比例することは誰もが知っているので、長年の勤勉な作業によって、専門家は頻繁にテストされた知識を参考のためにNCP-AII実践資料に集めました。したがって、NCP-AIIトレーニング資料は、彼らの努力の成果です。 NCP-AIIの実践教材を使用することで、以前に想像していた以上の成果を絶対に得ることができます。 NCP-AIIの実際のテストを選択した顧客から収集された明確なデータがあり、合格率は98〜100％です。したがって、成功を収めるチャンスは、当社の資料によって大幅に向上します。

NVIDIA NCP-AII 認定試験の出題範囲：

トピック	出題範囲
トピック 1	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.
トピック 2	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
トピック 3	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.
トピック 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.
トピック 5	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.

NVIDIA AI Infrastructure 認定 NCP-AII 試験問題 (Q168-Q173):

質問 # 168
A system administrator is installing a GPU into a server and needs to avoid damaging the device. What item should be used?

A. Gloves
B. Anti-ESD strap
C. Protective film
D. Electric screwdriver

正解：B

解説：
High-performance NVIDIA GPUs, such as the H100 or A100, are highly sensitive toElectrostatic Discharge (ESD). A static spark that a human cannot even feel (less than 3,000 volts) is enough to permanently damage the microscopic circuits within the GPU die or the HBM (High Bandwidth Memory) modules. AnAnti-ESD strap(or wrist strap) is the mandatory safety item for any technician handling internal server components. It works by grounding the technician, ensuring that any static charge built up on their body is safely dissipated before they touch the hardware. While gloves (Option B) might protect against sharp edges, they do not prevent ESD unless they are specifically rated as ESD-safe. Using an electric screwdriver (Option D) is generally discouraged for sensitive components to prevent over-tightening or mechanical stress. Therefore, an ESD strap is the single most critical tool for preventing "Infant Mortality" of expensive AI hardware during physical installation.

質問 # 169
You are configuring an InfiniBand subnet with multiple switches. You need to ensure that traffic between two specific nodes always takes the shortest path, bypassing a potentially congested link. Which of the following approaches is MOST effective for achieving this using InfiniBand's routing capabilities?

A. Decrease the MTIJ size on the potential congested link.
B. Use static routing by manually configuring forwarding tables on each switch along the desired path. This involves specifying DLID-to-Port mappings.
C. Rely solely on the Subnet Manager's (SM) default path computation algorithm (e.g., Min Hop) without any modifications.
D. Utilize the ibroute command or similar tool to inject a static route between the nodes, forcing traffic to follow a specific path identified by LID and port number.
E. Implement Quality of Service (QOS) to prioritize the traffic between the two nodes, hoping that this will influence the path selection.

正解：D

解説：
Static routing with 'ibroute' (or similar) provides the most direct and reliable way to ensure traffic follows a specific path. The SM's default algorithm might not always choose the optimal path, and QOS only prioritizes traffic, not forces a specific route. Configuring forwarding tables manually on each switch is error-prone and difficult to manage at scale.

質問 # 170
You are running a Docker container with GPU support using 'nvidia-docker run'. The containerized application unexpectedly fails to detect the GPU. What is the most likely cause?

A. The Docker image does not include the CUDA toolkit.
B. The =gpus all' flag was not specified when running the container.
C. The application within the container is not linked against the CUDA libraries.
D. The Docker daemon is not configured to use the NVIDIA runtime.
E. The NVIDIA drivers are not installed on the host system.

正解：B

解説：
When running containers that need GPU access, it's essential to explicitly request the GPU resources. The '-gpus all' or '-gpus device=..: flag passed to 'docker run' with the NVIDIA runtime allows the container access to the available GPUs. Without this flag, the container operates as if no GPUs are available. Options A, B, C and D, while potentially problematic, are not the most likely cause if 'nvidia-docker run' was used previously.

質問 # 171
After configuring HA, the administrator runs cmsh status and notices the secondary head node reports mysql
[FAIL]. What is the most likely cause?

A. The secondary head node lacks NVIDIA GPU drivers.
B. The cluster nodes are powered on during the HA configuration.
C. Network connectivity issues between the primary and secondary head nodes.
D. The BCM license expired after HA configuration.

正解：C

解説：
In a Bright Cluster Manager HA setup, the database (MySQL/MariaDB) must remain perfectly synchronized between the active and standby head nodes to allow for a seamless transition. This synchronization typically occurs over a dedicated management or heartbeat network. If cmsh status shows the database service as
[FAIL] on the secondary node, it almost always points to a communication breakdown. Without a stable network path, the secondary node cannot receive the binary logs from the primary node to keep its local copy up to date. While licensing (Option A) is important, a license failure usually disables management capabilities entirely rather than just the MySQL sync. Furthermore, head nodes are management servers and do not require GPU drivers (Option C) for their primary function. Ensuring low-latency, reliable connectivity between the two head nodes is the primary troubleshooting step for resolving "MySQL FAIL" states in BCM.

質問 # 172
Which of the following statements are correct regarding the use of NVIDIA GPUs with Docker containers?

A. You must install NVIDIA drivers inside the Docker container to enable GPU support.
B. Using environment variables like 'CUDA VISIBLE DEVICES' within the container can influence which GPUs are accessible to the application.
C. The 'nvidia-smr command can only be run on the host machine, not inside a Docker container.
D. CUDA libraries are required inside the container if your application uses CODA.
E. The NVIDIA Container Toolkit allows you to run GPU-accelerated applications in Docker containers without modifying the container image.

正解：B、D、E

解説：
The NVIDIA Container Toolkit allows GPU-accelerated apps to run in Docker without altering the image. The host's drivers are leveraged. CUDA libraries are necessary inside the container if your app uses CUDA. is used to control GPU visibility within the container. Drivers are not needed inside the container because they're managed by the host (making B incorrect), and 'nvidia-smi' can be run inside containers if the NVIDIA Container Toolkit is properly set up (making C incorrect).

質問 # 173
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