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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 2	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.
Topic 3	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 4	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 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.

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NVIDIA AI Infrastructure Sample Questions (Q61-Q66):

NEW QUESTION # 61
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. Delete /etc/X11/xorg.conf to force driver reconfiguration.
B. Disable Secure Boot permanently in BIOS/UEFI settings.
C. Reinstall drivers using apt-get install nvidia-driver-550 without rebooting.
D. Enroll the Machine Owner Key (MOK) during system reboot and enter the recorded password.

Answer: D

Explanation:
UEFI Secure Boot is a security standard that ensures only digitally signed code is allowed to execute during the boot process. Since NVIDIA GPU drivers include kernel modules (nvidia.ko), they must be signed by a key trusted by the system's firmware. When drivers are installed on a DGX system with Secure Boot active, the installation process generates a uniqueMachine Owner Key (MOK). However, the Linux kernel will not trust this key until the user manually authenticates it at the "Shim" level before the OS loads. Upon the first reboot after installation, the system enters the "MOK Management" blue screen. The administrator must select
"Enroll MOK" and enter the temporary password created during the driver installation. Failing to do this results in the kernel rejecting the nvidia module, leading to an "Unable to determine the device handle for GPU" error in nvidia-smi. Disabling Secure Boot (Option A) would resolve the symptom but violates the security posture of the AI infrastructure.

NEW QUESTION # 62
A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. 'nvidia-smi' reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others. What is the MOST likely cause and the immediate action to take?

A. A software bug in the CUDA toolkit is causing the errors; downgrade to an earlier version.
B. The power supply is failing to provide sufficient power to GPUs 3 and 4; replace the power supply.
C. The temperature sensors on GPUs 3 and 4 are faulty; replace the GPUs immediately.
D. There is a localized airflow problem affecting GPUs 3 and 4; check fan speeds and airflow obstructions.
E. A driver issue is causing incorrect temperature reporting; reinstall the NVIDIA driver.

Answer: D

Explanation:
Elevated inlet temperatures, despite normal GPU temperatures, strongly suggest an airflow issue. GPUs 3 and 4 are likely positioned in a way that restricts airflow. The first step is to check fan speeds and for any physical obstructions blocking airflow. Replacing components without addressing the airflow issue will not solve the problem.

NEW QUESTION # 63
You are preparing a GPU cluster for distributed AI training. Before running workloads, you need to validate the cluster's hardware. Which of the following steps is the most effective?

A. Run single-node benchmarks to confirm GPU performance on individual nodes.
B. Verify CPU performance across all nodes, as it indicates overall system stability.
C. Confirm that all nodes boot successfully into the operating system without errors.
D. Use NCCL tests to validate GPU-to-CPU communication and run node health checks.

Answer: A

Explanation:
Single-node GPU benchmarks validate that each node's GPUs are functioning correctly and delivering expected performance before distributed training begins. This establishes the hardware baseline needed before moving on to multi-node communication and workload testing.

NEW QUESTION # 64
Which of the following methods are considered the most reliable ways to install NVIDIA drivers on a production server running a stable Linux distribution (e.g., RHEL, CentOS, or Ubuntu LTS) to minimize downtime and ensure system stability?

A. Building custom NVIDIA driver packages from source code to match the specific kernel version and system libraries.
B. Utilizing NVIDIA's container toolkit and running all A1 workloads inside containers to isolate the driver dependencies.
C. Using NVIDIA's data center driver program.
D. Downloading and running the latest .run' installer from the NVIDIA website without any package manager integration.
E. Using the distribution's package manager (e.g., 'apt' , 'yum' , or 'dnf) to install NVIDIA drivers from the official or curated repositories.

Answer: B,C,E

Explanation:
Using the distribution's package manager is generally the safest and most reliable method for installing NVIDIA drivers. This approach ensures that dependencies are managed correctly and updates are handled through the system's standard update mechanisms. Containerization isolates the driver and application dependencies. Nvidia's data center driver program provides enterprise-grade support and reliability. Running the .run installer directly can sometimes lead to dependency issues and conflicts. Building from source is complex and not generally recommended for production environments.

NEW QUESTION # 65
You are an infrastructure engineer tasked with validating a new AI training cluster before releasing it to users. Your team wants to perform a NeMo burn-in to ensure both hardware and software are reliable and ready for production workloads. Which of the following actions are required as part of a proper NeMo burn-in process? (Choose two.)

A. Test inference using the NeMo API and approve the environment if the model outputs valid predictions.
B. Configure a representative NeMo training or pretraining recipe and set up an executor to launch the job across intended nodes and GPUs.
C. Run the configured NeMo training job repeatedly or for an extended duration, monitoring for errors, stalls, or performance drops across all GPUs and nodes.
D. Download a pre-trained NeMo model and use it for a quick accuracy check on a user dataset, then consider the burn-in complete if results are reasonable.

Answer: B,C

Explanation:
A proper NeMo burn-in uses a representative NeMo training or pretraining workload configured to run across the target GPUs and nodes. Running that workload for an extended duration while monitoring errors, stalls, GPU behavior, and performance stability validates that the hardware, drivers, containers, networking, and software stack are production-ready.

NEW QUESTION # 66
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