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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
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
  • 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 (Q147-Q152):

NEW QUESTION # 147
An infrastructure engineer is preparing a new AI cluster for production use, which relies on NVIDIA switches and high-speed optical transceivers for node connectivity. The team is finalizing network validation before launching large-scale training jobs. Why is it critical to confirm and align the firmware version on all switch transceivers prior to production?

Answer: C

Explanation:
Matching supported transceiver firmware versions across the cluster helps ensure link stability, expected bandwidth, and compatibility between NVIDIA switches and optical modules. This reduces the risk of link flaps, degraded throughput, and intermittent fabric issues during large- scale AI training workloads.


NEW QUESTION # 148
An administrator is configuring node categories in BCM for a DGX BasePOD cluster. They need to group all NVIDIA DGX H200 nodes under a dedicated category for GPU-accelerated workloads. Which approach aligns with NVIDIA's recommended BCM practices?

Answer: D

Explanation:
NVIDIA Base Command Manager (BCM) uses "Categories" as the primary organizational unit for applying configurations, software images, and security policies to groups of nodes. In a heterogeneous cluster-or even a large homogeneous one-creating specific categories for different hardware generations (like DGX H100 vs. H200) is a best practice. By creating a dedicated dgx-h200 category (Option B), the administrator can apply specific kernel parameters, driver versions, and specialized software packages (like specific versions of the NVIDIA Container Toolkit or DOCA) that are optimized for the H200's HBM3e memory and Hopper architecture updates. Using a generic dgxnodes category (Option C) makes it difficult to perform rolling upgrades or test new drivers on a subset of hardware without impacting the entire cluster. Furthermore, categorizing nodes allows for more granular integration with the Slurm workload manager, enabling users to target specific hardware features via partition definitions that map directly to these BCM categories. This modular approach reduces "configuration drift" and ensures that the AI factory remains manageable as it scales from a single POD to a multi-POD SuperPOD architecture.


NEW QUESTION # 149
A data scientist wants to run a PyTorch container on docker using a Multi-Instance GPU (MIG) slice. Based on the requirements, the system administrate suggests using a MIG slice with 2g compute profile and 10GB of GPU memory. What command should the administrator tell the data scientist to run?

Answer: B

Explanation:
Docker uses the --gpus option with a device= selector to expose a specific GPU or MIG device to the container. The device=0:2 format targets a MIG device on GPU 0, allowing the PyTorch container to run using that assigned MIG slice.


NEW QUESTION # 150
A system administrator wants to check the overall health status for their DGX systems. What command should be used?

Answer: D

Explanation:
nvsm is the NVIDIA System Management tool used on DGX systems to check overall platform health, including hardware, sensors, storage, power, and GPU-related system status.


NEW QUESTION # 151
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?

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 # 152
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