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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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.

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NVIDIA AI Infrastructure Sample Questions (Q79-Q84):

NEW QUESTION # 79
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. Keep the older driver running alongside the new version in case you need to roll back the upgrade.
B. Validate the driver version post-install since the fresh install will overwrite the legacy drivers.
C. Uninstall all existing GPU and DOCA-related drivers and associated kernel modules before the new install
D. Update the GPU driver leaving the DOCA and OFED drivers unchanged as long as they are detecting the hardware properly.

Answer: C

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 # 80
Which software library provides GPU acceleration for pandas-like DataFrame operations?

A. TensorFlow Data API
B. Intel oneAPI Data Analytics Library
C. R tidyverse
D. RAPIDS cuDF

Answer: D

Explanation:
RAPIDS cuDF provides GPU-accelerated DataFrame operations with a pandas-like API, enabling faster data processing on NVIDIA GPUs for analytics and AI workflows.

NEW QUESTION # 81
You are setting up a new AI inference server in a colocation facility. The server will be connected to a 100GbE switch managed by the facility. You have the option to use either a single-mode fiber connection with an LR4 transceiver or a multi-mode fiber connection with an SR4 transceiver. The distance between your server and the switch is approximately 75 meters. Considering cost, signal quality, and future scalability, which option is MOST suitable?

A. LR4 transceiver with single-mode fiber, as it provides better power efficiency.
B. LR4 transceiver with single-mode fiber, as it provides better signal quality over distance.
C. Either option is equally suitable; the choice is arbitrary.
D. SR4 transceiver with multi-mode fiber, as it is typically more cost-effective for shorter distances.
E. AOC cable, as it provides better future scalabilty than any other type of connection

Answer: D

Explanation:
For a distance of 75 meters, an SR4 transceiver with multi-mode fiber is the most suitable option. It is typically more cost-effective than LR4 transceivers and provides adequate signal quality for this distance. LR4 transceivers are designed for longer distances and are more expensive. While AOCs are convenient, SR4 with multimode fiber is more cost effective. Cost-effectiveness is the defining factor between them.

NEW QUESTION # 82
You are tasked with installing the latest NVIDIA driver on a server running Ubuntu 22.04 for A1 workloads. You have downloaded the driver package 'NVIDIA-Linux-x86 64-535.104.05.run'. Before installation, what is the most critical step to ensure a smooth process, assuming secure boot is enabled?

A. Install the driver using 'apt install nvidia-driver-535' and let the system handle Secure Boot automatically.
B. Create a DKMS module and sign the driver with a machine owner key (MOK) for Secure Boot compatibility.
C. Disable Secure Boot in the BIOS before installing the driver.
D. Simply run the ' .run' file using 'sudo ./NVlDlA-Linux-x86_64-535.104.05.run' .
E. Ensure the 'nouveau- driver is blacklisted by adding 'blacklist nouveau- to 'letc/modprobe.d/blacklist-nouveau.conf.

Answer: B,E

Explanation:
Secure Boot requires kernel modules to be signed. Directly running the .run' file or disabling Secure Boot are generally not recommended. DKMS and MOK signing allows the driver to be validated by the system. Blacklisting nouveau ensures that it won't conflict with the NVIDIA driver. Installing via apt doesn't guarantee Secure Boot compatibility without further steps, so the best approach involves DKMS/MOK and blacklisting.

NEW QUESTION # 83
During the setup of a new GPU cluster, you want to validate the GPUs to ensure they meet performance and reliability standards. What would demonstrate performance standards are met?

A. Use DCGM diagnostics and gpu-burn to stress test GPUs under load.
B. Verify that the GPU type and count match expected specifications.
C. Validate NVLink bandwidth between GPUs by running data transfer tests across all connected nodes.
D. Test a small reference task, such as training a simple model, to confirm general functionality.

Answer: A

Explanation:
DCGM diagnostics and gpu-burn validate GPU reliability and performance under sustained load.
These tools stress the GPUs, monitor health and error conditions, and help confirm that the devices can operate within expected performance and stability standards before production use.

NEW QUESTION # 84
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