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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	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 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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
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 (Q64-Q69):

NEW QUESTION # 64
You have installed the NVIDIA Container Toolkit and are attempting to run a container with GPU support. However, the 'docker run' command fails with an error indicating that the NVIDIA runtime is not found. You have already verified that the NVIDIA Container Toolkit is installed, and the Docker daemon has been restarted. What is the most likely cause of this error?

A. The 'nvidia-container-runtime' package is not installed.
B. The NVIDIA driver version is incompatible with the CUDA version specified in the container image.
C. The system doesn't have a GPU.
D. The container image is corrupted and needs to be rebuilt.
E. The '/etc/docker/daemon.json' file is missing or has incorrect configuration settings related to the NVIDIA runtime.

Answer: E

Explanation:
The most likely cause is an issue with the S/etc/docker/daemon.json' file (A). This file configures Docker's runtime settings, including specifying the NVIDIA runtime. If the file is missing or has incorrect entries, Docker will not be able to find the NVIDIA runtime. While driver incompatibility (B) can cause issues, it typically manifests as runtime errors within the container, not a failure to find the runtime itself. 'nvidia- container-runtime' might be a required package depending on the installation method. A missing GPU is unlikely since the Toolkit would likely fail to install, although this is also an error that can prevent the NVIDIA runtime from being started.

NEW QUESTION # 65
You are deploying an NVIDIA-Certified A1 server. The documentation specifies a minimum airflow requirement for the GPUs. How would you BEST monitor the GPU temperatures and ensure the airflow is adequate during a stress test?

A. Use a software utility like 'psensor' to monitor GPU temperature.
B. Use a thermal camera to measure the GPU heatsink temperature.
C. Use 'nvidia-smi' to monitor GPU temperature and visually inspect the fans.
D. Use IPMI sensors to monitor GPU temperature and fan speeds.
E. Measure the ambient temperature around the server.

Answer: D

Explanation:
IPMI provides remote monitoring of hardware sensors, including GPU temperature and fan speeds, allowing you to ensure the cooling system is working correctly during a stress test. 'nvidia-smi' gives the GPU temp but not the fan speed. Ambient temperature isn't an accurate reflection of the GPU's actual temperature.

NEW QUESTION # 66
A 24-hour HPL burn-in fails with "illegal value" errors during the first iteration. Which initial troubleshooting step resolves this without compromising burn-in validity?

A. Disable GPU affinity.
B. Reduce test duration to 12 hours.
C. Switch from FP64 to FP32 precision.
D. Verify the matrix size is divisible by block size.

Answer: D

Explanation:
HPL "illegal value" errors at the first iteration commonly indicate an invalid HPL.dat configuration.
Verifying that the matrix size is compatible with the block size corrects the parameter issue while preserving the intended burn-in workload duration and precision.

NEW QUESTION # 67
An engineer wants to verify that an NVIDIA GPU is accessible inside a Docker container for running deep learning workloads. The NVIDIA Container Toolkit is installed on a machine with working NVIDIA drivers.
Which command demonstrates the correct way to run a container that can access all available GPUs?

A. docker run --rm --gpus all nvidia/cuda:12.4.6-base-ubuntu22.04 nvidia-smi
B. docker run --rm --runtime=docker nvidia/cuda nvidia-smi
C. docker run --rm -it ubuntu:22.04 nvidia-smi
D. docker run --rm nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi

Answer: A

Explanation:
The correct command is docker run --rm --gpus all nvidia/cuda:12.4.6-base-ubuntu22.04 nvidia-smi. With the NVIDIA Container Toolkit installed and Docker configured, the --gpus all flag is the standard way to expose all available NVIDIA GPUs to a container. Running nvidia-smi inside the CUDA base container confirms that the container can see the host GPU driver stack, GPU devices, and NVIDIA runtime integration. Option A is incorrect because --runtime=docker is not the correct NVIDIA runtime configuration and does not request GPU access. Option B uses a plain Ubuntu container, which normally lacks the NVIDIA user-space libraries needed to run nvidia-smi. Option D uses an NVIDIA CUDA image but does not include the --gpus all flag, so Docker will not automatically expose GPUs to the container. In NVIDIA AI infrastructure, this validation is a basic control-plane and container-runtime check before running AI workloads through Docker, Kubernetes, Slurm containers, or NVIDIA GPU Operator-managed environments.

NEW QUESTION # 68
A systems administrator is preparing a new DGX server for deployment. What is the most secure approach to configuring the BMC port during initial setup?

A. Enable remote access to the BMC over the internet using the default admin credentials for initial troubleshooting.
B. Connect the BMC port directly to the production network and retain default admin credentials for convenience.
C. Leave the BMC port disconnected until after the operating system is fully configured and in production.
D. Connect the BMC port to a dedicated and firewalled network and change the default admin credentials.

Answer: D

Explanation:
The Baseboard Management Controller (BMC) is a powerful tool that allows for total control over the DGX system, including the ability to flash firmware, cycle power, and access the serial console. Because of this, it is a high-value target for security threats. The "100% verified" secure approach (Option D) involves two critical layers:
* Network Isolation: The BMC port should never be exposed to the public internet (Option A) or even the general production network (Option B). It must reside on adedicated Out-of-Band (OOB) network that is firewalled and accessible only to authorized administrators.
* Credential Management: Standard NVIDIA factory defaults (like admin/admin) must be changed immediately upon first access. As part of the DGX first-boot wizard, the system prompts the administrator to create a strong, unique password for the primary user, which is then synchronized to the BMC.
Leaving the port disconnected (Option C) is unfeasible for modern data center operations, as the BMC is required for remote monitoring and "headless" deployment. Following the isolated/firewalled approach ensures the AI Factory remains resilient against both external attacks and internal lateral movement.

NEW QUESTION # 69
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