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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Control Plane Installation and Configuration	19%	- Drivers and toolkits 1. NGC CLI deployment 2. NVIDIA GPU and DOCA drivers installation/update 3. NVIDIA container toolkit installation - Infrastructure software stack deployment 1. Cluster setup (Slurm, Enroot, Pyxis) 2. Base Command Manager (BCM) installation and HA configuration 3. Operating system installation - GPU utilization in containers 1. Docker GPU usage validation
Physical Layer Management	5%	- Networking and GPU partitioning 1. BlueField network platform configuration 2. MIG (Multi-Instance GPU) configuration
Troubleshoot and Optimize	12%	- Fault detection and remediation 1. Replacement of faulty hardware components 2. GPU, fan, network card fault identification - Performance optimization 1. Server performance tuning (Intel/AMD platforms) 2. Storage optimization
System and Server Bring-up	31%	- Hardware initialization and configuration 1. GPU server installation and validation 2. Firmware upgrades including HGX and fault detection 3. BMC, OOB, and TPM initial configuration 4. Hardware validation for workloads - Physical infrastructure validation 1. Power and cooling validation 2. Cable and transceiver types validation 3. Storage parameter initialization - Deployment and validation lifecycle 1. Network topologies for AI factories 2. Sequence of deployment and validation events
Cluster Test and Verification	33%	- Network and hardware validation 1. Cabling and signal verification 2. Firmware validation (switches, transceivers, BlueField) 3. NVLink validation - Performance and stress testing 1. Cluster burn-in tests (HPL, NCCL, NeMo) 2. HPL (High-Performance Linpack) benchmarking 3. Single-node stress testing 4. NCCL communication testing - Cluster diagnostics 1. ClusterKit multi-node assessment 2. Storage testing

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NVIDIA AI Infrastructure Sample Questions (Q119-Q124):

NEW QUESTION # 119
You are tasked with updating both NVIDIA GPU drivers and DOCA drivers on a set of servers used for AI workloads. The environment previously had an older driver stack and custom kernel modules. What is the most important step to successfully upgrade the drivers without causing conflicts?

A. Uninstall all existing GPU and DOCA-related drivers and associated kernel modules before the new install
B. Keep the older driver running alongside the new version in case you need to roll back the upgrade.
C. Update the GPU driver leaving the DOCA and OFED drivers unchanged as long as they are detecting the hardware properly.
D. Validate the driver version post-install since the fresh install will overwrite the legacy drivers.

Answer: A

Explanation:
Removing the existing GPU, DOCA, OFED, and related kernel-module stack before installing the new versions prevents version mismatches, stale kernel modules, and dependency conflicts. This is especially important when the servers previously used an older stack with custom modules.

NEW QUESTION # 120
An administrator installs NVIDIA GPU drivers on a DGX H100 system with UEFI Secure Boot enabled. After reboot, the drivers fail to load. What is the first action to resolve this issue?

A. Reinstall drivers using apt-get install nvidia-driver-550 without rebooting.
B. Delete /etc/X11/xorg.conf to force driver reconfiguration.
C. Disable Secure Boot permanently in BIOS/UEFI settings.
D. Enroll the Machine Owner Key (MOK) during system reboot and enter the recorded password.

Answer: D

Explanation:
With UEFI Secure Boot enabled, NVIDIA kernel modules must be trusted before they can load.
Enrolling the Machine Owner Key during reboot and entering the recorded password completes the trust process so the signed NVIDIA driver modules can load successfully.

NEW QUESTION # 121
You are tasked with validating the cooling system for a high-density AI cluster using NVIDIA Blackwell GPUs, which generate up to 120 kW of power per rack. What should you prioritize during validation?

A. Ensure that dynamic fan speed control is enabled to optimize airflow based on real-time thermal conditions.
B. Verify that each rack has sufficient airflow to handle 50% of its maximum heat load, as full load scenarios are rare.
C. Confirm that the cooling system uses liquid-to-air heat exchangers to manage heat dissipation efficiently.
D. Validate that the cooling system can maintain a supply air temperature below 20°C at all times to ensure GPU stability.

Answer: C

Explanation:
Blackwell rack-scale systems can generate extremely high heat loads, so cooling validation must confirm that the facility cooling architecture can efficiently remove that heat at full rack density.
Liquid-to-air heat exchangers are designed to support high-density AI racks by transferring heat from liquid cooling loops into the facility air-cooling environment.

NEW QUESTION # 122
A system administrator is installing a GPU into a server and needs to avoid damaging the device. What item should be used?

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

Answer: B

Explanation:
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.

NEW QUESTION # 123
You are installing multiple NVIDIA GPUs in a server for a deep learning cluster. To optimally utilize the GPUs, which software component(s) are MANDATORY after the physical installation and driver setup? (Select TWO)

A. NVIDIA CUDA Toolkit.
B. A spreadsheet program.
C. A deep learning framework like TensorFlow or PyTorch.
D. A web browser.
E. A text editor.

Answer: A,C

Explanation:
The NVIDIA CUDA Toolkit provides the necessary libraries and tools for GPU-accelerated computing. A deep learning framework (TensorFlow or PyTorch) is required to build and train deep learning models that leverage the GPUs. While a web browser and text editor might be useful, they are not mandatory. Spreadsheet applications have no purpose here.

NEW QUESTION # 124
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