Nutanix Nutanix Certified Professional - Artificial Intelligence NCP-AI 6.10 NCP-AI Exam Dumps: Updated Questions & Answers (December 2025)

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TheEndpoint Detailspage in the Nutanix Enterprise AI dashboard contains specific widgets for monitoring the performance of a single inference service.4

Number of Instances Widget:This specific widget displays the scaling configuration and resource consumption of the endpoint. It explicitly lists theNumber of vCPUsand theMemory usage per instancethat is actively in use to serve the endpoint.5

Distinction:The "Infrastructure Summary" widget is located on the main Dashboard (not the Endpoint Details page) and displayscluster-wideresource usage, not the specific memory footprint of a single endpoint.

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

To identify the source of traffic spikes when multiple applications share a single inference endpoint, the administrator must isolate the clients.

Isolation Strategy:Each application connecting to the Nutanix Enterprise AI (NAI) endpoint uses a uniqueAPI Keyfor authentication. By deactivating these keys one at a time, the administrator can observe when the "huge increase in requests" drops, thereby positively identifying the specific application responsible for the load.

Why other options fail:

Increasing resources (Option B) might mitigate the slowness but does notidentifythe offending application.

Reducing resources (Option C) would worsen the performance issues.

Deactivating the endpoint (Option D) takes the service offline for everyone, failing to isolate the specific culprit.

Note:While the NAI dashboard does provide usage metrics that can be filtered by API key to view request counts, among the actions listed in the question, deactivating keys is the specificactivetroubleshooting step available to isolate a client if metric analysis is not an option or is inconclusive.

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In the Nutanix Enterprise AI (NAI) endpoint lifecycle, the Pending status has a specific definition related to infrastructure resource availability. According to the endpoint management documentation:

"Pending: The system is waiting for the resources required to create the endpoint. For example, if the required number of GPUs are not available, Nutanix Enterprise AI maintains a Pending status until the GPUs become available."

The exhibit shows the administrator explicitly requested1 NVIDIA-H100-NVLGPU for the instance. If the underlying Kubernetes cluster does not have a free H100 GPU (e.g., all H100s are currently occupied by other active endpoints or training jobs), the scheduler cannot place the pod, and the status will remain "Pending" indefinitely until a GPU is freed.

In contrast:

Unauthorized NGC Keystypically result in aFailedstatus due to image pull errors (ImagePullBackOff) or authentication failures, not a resource-wait "Pending" state.

Permissions issueswould generally prevent the creation wizard from completing or result in an immediate 403 Forbidden error upon submission.

Wrong GPU selection(compatibility) is unlikely here as the "gemma-2-2b-it" is a small model that can easily run on the powerful H100; the issue is simply that the requested hardware is currently fully utilized.

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According to the Nutanix Enterprise AI installation prerequisites, the administrator must configure a specific storage class for the persistent volumes that store Large Language Models (LLMs) and model data. The documentation explicitly states: "Ensure that you create a ReadWriteMany (RWX) storage class that enables static or dynamic provisioning of NFS shares for persistent volumes."

ThisReadWriteManyaccess mode is mandatory because the AI models stored on these volumes must be simultaneously accessible (readable and writable) by multiple pods running on different worker nodes across the Kubernetes cluster. This shared access ensures that as inference endpoints scale out to multiple replicas, all instances can mount and utilize the same underlying model files without locking issues.

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In the context of the Nutanix Enterprise AI (NAI) deployment workflow, the user hierarchy is strictly defined to ensure security and proper access control during the initialization of the platform.

TheSuper Adminis the specific role assigned to the first user who installs and initializes Nutanix Enterprise AI on the target Kubernetes cluster. This role is distinct from subsequent administrative roles.

According to the user management documentation for Nutanix Enterprise AI:

Initialization:The installation process automatically designates the deploying user as the Super Admin.

Privileges:The Super Admin holds the highest level of authority. This includes the exclusive ability to create and manage other administrators (designated asAI/ML Admins) and standard users (AI/ML Users).

Token Management Distinction:A critical differentiator found in the documentation is that while an AI/ML Admin can only update or delete third-party tokens (such as those for Hugging Face) that they created themselves, theSuper Adminhas the elevated privilege to add, update, or delete tokens created byanyuser in the system.

Therefore, the account that bootstraps the installation is always the Super Admin.

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In the Nutanix Enterprise AI user interface, the management of API keys is located within the Endpoints section. To deactivate a key, the administrator must navigate to the Endpoints page from the left navigation pane.

Once on the Endpoints page, there is a specific button or tab labeledAPI Keys. Clicking this opens the API Keys list view. The administrator can then select the specific key (via checkbox) and use theActionsdropdown menu to selectDeactivate. This location is distinct from the "Settings" or "Admin Configuration" menus.

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

To make a Large Language Model (LLM) available for deployment in Nutanix Enterprise AI, the administrator must use the **Models** section within the Nutanix Enterprise AI (NAI) dashboard. The correct workflow involves using the **Import Model** interface.

Within this interface, the administrator can choose to import a model directly from the **Hugging Face** model hub (by providing the repository name and optional token) or perform a **Manual Import** (Upload) for custom or air-gapped models. This action registers the LLM in the NAI repository, changing its status to "Ready," which is the prerequisite for deploying it as an inference endpoint. Options regarding Prism Central marketplace or third-party orchestration tools are incorrect for the specific task of ingesting the model *into* the NAI platform.

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NVIDIA NIMs (NVIDIA Inference Microservices) are specialized, pre-optimized containers designed specifically to leverage NVIDIA GPU architecture (CUDA) for accelerated inference.

Hardware Dependency:According to the NAI documentation regarding NVIDIA NIM imports, these models have a strict dependency on GPU resources to function. Unlike generic Hugging Face models which might optionally run on CPUs (albeit slowly), NIMs are engineered for GPU execution.

UI Behavior:When deploying a model identified as a NIM, the Nutanix Enterprise AI interface automatically detects this requirement. Consequently, theUse GPUsetting is enforced (locked and selected) because the modelcannotrun on CPU-only nodes. The system ensures that the endpoint is scheduled only on worker nodes that possess the required NVIDIA accelerators and drivers.

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The symptoms described (CPU usage at 100%, pods waiting for resources/pending state, critical infrastructure health) indicate that the underlying Kubernetes cluster has run out of compute capacity (nodes).

Nutanix Enterprise AI (NAI) runs as an applicationon top ofa Kubernetes platform, typically theNutanix Kubernetes Platform (NKP). NAI itself manages the scaling ofinference endpoints(pods), but it relies on the underlying platform to provide the physical or virtual compute nodes. To resolve a cluster-wide resource exhaustion where CPU is 100% saturated, the administrator must expand the cluster's capacity. The most appropriate and automated way to handle this is toenable automatic resource scaling (Cluster Autoscaler) within NKP. This allows the platform to automatically provision additional worker nodes when pending pods cannot be scheduled due to resource constraints, thereby satisfying the demand for the Custom Chatbot service.

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Nutanix Enterprise AI utilizes Role-Based Access Control (RBAC) to manage permissions.6

AI/ML Admin:This role has full privileges over the NAI platform, including the ability tocreate new users, assign roles (Admin vs.7User), manage global quotas, and view all endpoints and models across the system.

AI/ML User:This role is restricted to managing only theirownresources (models, endpoints, and keys) and cannot create other user accounts or view resources belonging to other users.