Pass the Nutanix Certified Professional (NCP) NCP-AI Questions and answers with Dumpstech

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

To onboard a custom or "unvalidated" model directly from Hugging Face into the Nutanix Enterprise AI dashboard, administrators utilize theImport Modelworkflow.

Workflow:In the "Import Model" wizard, there is a specific option to import fromHugging Face.1This requires the user to input theRepository ID(often referred to in generic UI options as the "URL" or path to the model, e.g., meta-llama/Llama-2-7b).

Why D is correct:This option allows the platform to pull the model weights and configuration files directly from the specified public repository.

Why others are incorrect:

Using CURL (A):NAI provides cURL code snippets toconsume(make inference requests to) a running model, not toimportit into the registry.

NGC Catalog (B):This is for NVIDIA-specific models, not general Hugging Face repositories.

Marketplace app (C):This typically refers to Nutanix Marketplace blueprints, not the specific action of importing a raw model weight set from Hugging Face.

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

To successfully deploy and run AI workloads with acceleration on the Nutanix Enterprise AI platform, the physical and software layer of the worker nodes must meet specific GPU requirements.

Hardware:The Kubernetes worker nodes must be equipped with supportedNVIDIA GPUs(such as NVIDIA A100, H100, or L40S).

Drivers:The appropriateNVIDIA driversmust be installed on these nodes. In NAI deployments (especially on NKP), this is often managed by theNVIDIA GPU Operator, which automates the deployment of the driver, container toolkit, and device plugin.

Validation:If the nodes lack these supported GPUs or if the drivers are missing/incompatible, the GPU resources will not be advertised to the Kubernetes scheduler, and AI models requiring acceleration will fail to schedule (remaining in a Pending state).

Options A, B, and C are incorrect because CVMs do not use GPU passthrough (the worker VMs do), specific legacy Prism versions are not the primary hardware prerequisite for GPU usage, and while high-speed networking (100GbE) is recommended for performance, it is not a hard functional prerequisite for the GPU hardware itself to operate.