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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional AI Infrastructure Exam |
| Exam Number: | NCP-AII |
| Exam Duration: | 120 minutes |
| 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: | 60-70 |
| Exam Price: | $400 USD |
| Exam Format: | Multiple-choice questions, Scenario-based items |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Passing Score: | Pass/Fail only, no numerical score |
| Recommended Training: | NVIDIA AI Infrastructure Training |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online remote proctored or onsite at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended 2–3 years of experience in data center infrastructure, Linux administration, and NVIDIA hardware/software environments |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-professional/ |
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NEW QUESTION # 128
You are managing a cluster of servers with NVIDIA GPUs. You want to automate the process of installing and updating NVIDIA drivers across all nodes. Which of the following approaches would be the most scalable and maintainable?
Answer: A,C
Explanation:
Configuration management tools (Ansible, Chef, Puppet) provide the most scalable and maintainable solution. They allow you to define the desired state of the system and automatically enforce it across all nodes. NVIDIA's container toolkit encapsulates the driver and provides a consistent, isolated environment. Manually SSHing is not scalable. While 'pdsh' and 'parallel' can automate the execution of commands, they lack the robust error handling and state management of configuration management tools. NFS distribution requires managing the installation process separately on each server.
NEW QUESTION # 129
You are designing an AI infrastructure using NVIDIA HGX AIOO servers. These servers support both PCle Gen4 and NVLink for GPU interconnects. Which statement is MOST accurate concerning the choice between PCle Gen4 and NVLink for inter-GPU communication within a single HGX AIOO server for deep learning training?
Answer: D
Explanation:
NVLink is specifically designed for high-bandwidth, low-latency communication between GPUs, making it superior to PCle Gen4 for deep learning training where GPUs frequently exchange data. NVLink allows GPIJs to share memory directly.
NEW QUESTION # 130
What command sequence is used to identify the exact name of the server that runs as the master SM in a multi-node fabric?
Answer: D
Explanation:
In an InfiniBand fabric, the Subnet Manager (SM) is the "brain" of the network, responsible for discovering the topology, assigning Local Identifiers (LIDs), and calculating routing tables. In a multi-node fabric, there is typically one Master SM and several Standby SMs for high availability. To identify the master, the sminfo command is first used; it queries the fabric and returns the LID of the current Master SM. Once the LID is obtained, the engineer must map that numerical LID to a physical server name or Node Description. The smpquery ND (Node Description) command is then executed, targeting that specific LID. This sequence is vital for troubleshooting fabric-wide issues, as logs on the Master SM server provide the definitive record of sweeps, traps, and topology changes. Using smpquery NI (Node Info) would provide hardware-level details like the GUID and device ID, but it does not return the human-readable string (server name) defined in the Node Description, which is necessary for rapid identification in a crowded data center.
NEW QUESTION # 131
A system administrator wants to configure MIG for seven slices on an H100 GPU in an NVIDIA HGX system. Which command should be used?
Answer: C
Explanation:
The correct answer is mig-parted. NVIDIA MIG Partition Editor, commonly called mig-parted, is designed to automate and manage Multi-Instance GPU partitioning profiles, especially in environments where repeatable MIG layouts are required. An H100 GPU can be divided into multiple isolated GPU instances, and a seven- slice configuration is a common profile pattern for maximizing the number of smaller MIG instances on a single GPU. While nvidia-smi can enable MIG mode and manually create or destroy GPU instances, mig- parted is better suited when an administrator needs to apply defined MIG profiles consistently across HGX systems or multiple nodes. nvcc is the CUDA compiler and has no role in configuring MIG partitions. nvlink- config is not the correct utility for MIG profile configuration. In NVIDIA AI infrastructure, MIG configuration must be controlled carefully because Kubernetes device plugins, GPU Operator, Slurm GRES configuration, and tenant scheduling depend on consistent GPU instance layouts. Using mig-parted helps reduce manual errors and supports repeatable server bring-up for shared inference or multi-tenant AI environments.
NEW QUESTION # 132
An enterprise is deploying an AI Factory using NVIDIA DGX BasePOD architecture. The infrastructure team must ensure high availability and efficient data transfer between compute nodes. Which network topology should they implement for the InfiniBand fabric?
Answer: A
Explanation:
The correct answer is Fat-Tree topology with rail-optimized design. NVIDIA DGX BasePOD and similar AI Factory architectures rely on high-performance InfiniBand fabrics to support distributed GPU training, low- latency communication, and scalable bandwidth between compute nodes. A Fat-Tree topology provides multiple high-bandwidth paths between servers and switches, helping reduce oversubscription and improve bisection bandwidth. Rail-optimized design is especially important in DGX environments because each GPU or network adapter path can be aligned to a specific network rail, improving predictable communication behavior for NCCL collectives such as all-reduce and reduce-scatter. A simple ring topology is not suitable because it introduces unnecessary hop latency and limited failure tolerance. A single flat Ethernet network for all traffic does not meet the dedicated high-performance InfiniBand fabric requirement. A star topology with one central switch can become a bottleneck and single point of failure. For BasePOD-scale AI workloads, the fabric must support high availability, bandwidth consistency, and efficient East-West GPU communication, making a rail-optimized Fat-Tree the correct design.
NEW QUESTION # 133
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