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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
Related Certifications:	NCP-DES NCP-AI
Exam Format:	Multiple Choice, Multiple Select
Real Exam Qty:	50
Available Languages:	English
Passing Score:	700 (scale of 0-1000)
Certificate Validity Period:	2 years
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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100% Pass Quiz 2026 NVIDIA NCP-AII: NVIDIA AI Infrastructure – The Best Exam Topic

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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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	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.

NVIDIA AI Infrastructure Sample Questions (Q49-Q54):

NEW QUESTION # 49
A researcher wants to allow a container to use only two specific GPUs out of four available ones (GPU 1 and GPU 2) on the host. The system is already set up for NVIDIA Docker with the container toolkit. Which Docker command will correctly assign only these GPUs to the container?

A. docker run --rm --gpus 2 nvidia/cuda nvidia-smi
B. docker run --gpus '"device=0,3"' nvidia/cuda nvidia-smi --query-gpu=uuid --format=csv
C. docker run --gpus '"device=0,1"' nvidia/cuda nvidia-smi --query-gpu=uuid --format=csv
D. docker run --rm --gpus '"device=1,2"' nvidia/cuda nvidia-smi

Answer: D

Explanation:
The Docker --gpus option can restrict a container to specific GPU IDs. Specifying device=1,2 exposes only GPU 1 and GPU 2 to the container, and running nvidia-smi inside the CUDA container verifies that only those GPUs are visible.

NEW QUESTION # 50
A healthcare organization is deploying an AI system to analyze patient data for predictive diagnostics. The system must comply with strict data protection regulations such as HIPAA, ensuring that sensitive information remains confidential and secure. Considering the need for robust security measures, which combination of strategies should the organization prioritize to protect against data breaches and ensure regulatory compliance?

A. Use tokenization to replace sensitive data with non-sensitive tokens and employ multi-factor authentication (MFA) for system access.
B. Implement symmetric encryption for all data at rest and rely solely on password-based access controls.
C. Deploy data masking to obscure sensitive data during processing and use role-based access control (RBAC) to limit data access based on user roles.
D. Rely on asymmetric encryption for all communications and use data deduplication to minimize storage costs without additional security measures.

Answer: C

Explanation:
The best answer is data masking combined with role-based access control. In healthcare AI environments, patient data must be protected throughout ingestion, preprocessing, training, inference, analytics, and operational access. Data masking helps reduce exposure of sensitive patient identifiers or protected health information when full raw values are not required for a user or processing stage. RBAC ensures that only authorized users, services, or roles can access specific datasets, models, logs, or administrative functions. This is especially important in AI infrastructure where multiple teams may share GPUs, storage, notebooks, Kubernetes namespaces, or model registries. Tokenization and MFA are also valuable, but the option does not include fine-grained authorization over data access. Symmetric encryption at rest is important, but relying solely on passwords is not sufficient for regulated environments. Asymmetric encryption for communications is useful, but deduplication is a storage efficiency feature, not a primary security control. In NVIDIA AI infrastructure, healthcare deployments should combine RBAC, encryption, audit logging, data minimization, masking or de-identification, secure container access, and controlled administrative privileges.

NEW QUESTION # 51
Consider the following scenario: You have a BlueField-2 DPU installed in a server. You are trying to establish RDMA (Remote Direct Memory Access) communication between the DPU and another server. However, the RDMA connection fails. Which of the following is the most crucial factor to verify in this scenario?

A. That the power supply to the DPU is providing sufficient wattage.
B. That the appropriate RDMA kernel modules (e.g., , 'ib_uverbs) are loaded on both the DPUand the remote server.
C. That the clocks on both servers are synchronized using NTP (Network Time Protocol).
D. That the TCP window size is properly tuned for high-bandwidth communication.
E. That the correct MTU (Maximum Transmission Unit) is configured on the network interfaces involved in the RDMA connection.

Answer: B

Explanation:
For RDMA to function correctly, the necessary kernel modules must be loaded on both ends of the connection. These modules provide the core functionality for RDMA communication. While MTU and other network settings are important, the absence of the RDMA modules will prevent the connection from being established in the first place. Clock synchronization and TCP window size are less directly related to the initial RDMA connection failure. Power supply issues would likely manifest in other ways.

NEW QUESTION # 52
You are managing a server farm of GPU servers used for A1 model training. You observe frequent GPU failures across different servers.
Analysis reveals that the failures often occur during periods of peak ambient temperature in the data center. You can't immediately improve the data center cooling. What are TWO proactive measures you can implement to mitigate these failures without significantly impacting training performance?

A. Schedule training jobs to run during off-peak hours when ambient temperatures are lower.
B. Increase the fan speeds of the GPU coolers to improve heat dissipation.
C. Implement a more aggressive GPU frequency scaling profile to throttle performance during peak temperatures.
D. Reduce the GPU power limit using 'nvidia-smi' to decrease heat generation.
E. Replace all existing GPUs with water-cooled models.

Answer: A,D

Explanation:
Reducing the GPIJ power limit (A) will directly reduce heat generation, which is the primary driver of failures in this scenario. While it will slightly reduce performance, the impact is less than aggressive throttling. Scheduling training jobs for off-peak hours (D) completely avoids the issue of high ambient temperatures. Increasing fan speeds (B) can help, but it may not be sufficient and can increase noise. Frequency scaling (C) is a more aggressive approach that can significantly impact performance. Replacing all GPUs with water-cooled models (E) is a more expensive and complex solution that may not be immediately feasible.

NEW QUESTION # 53
What information does the 'ibnodes' command display?

A. All channel adapters
B. All host & server names
C. All hosts & switches
D. All server names

Answer: C

Explanation:
The ibnodes utility is a specialized script within the infiniband-diags suite used for high-level topology mapping. When executed, it scans the InfiniBand subnet and provides a concise list of every active node connected to the fabric, categorized by their type. Specifically, it displays all Host Channel Adapters (HCAs
/CA) found in the servers (the "hosts") and all InfiniBand switches. This command is an essential first step in fabric validation after a "bring-up" or expansion because it allows an engineer to quickly verify if the expected number of nodes and switches have successfully joined the fabric. Unlike ibnetdiscover, which provides a verbose and complex output of every port-to-port connection, ibnodes focuses on the identity and type of the devices. This distinction is critical for troubleshooting connectivity; if a server's HCA is missing from the ibnodes list, the engineer knows to investigate the physical link or the local HCA driver state rather than searching through thousands of lines of switch-to-switch cabling data.

NEW QUESTION # 54
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