NCP-AI Dumps With Exact Questions and Answers

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.

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

When deploying Nutanix Enterprise AI (NAI) on the Nutanix Kubernetes Platform (NKP), the storage architecture requires specific protocols for different types of data.

Model Repository Storage:For the storage of Large Language Models (LLMs)—whether they are pre-validated models downloaded from public hubs or custom models imported manually—the platform requires aReadWriteMany (RWX)compatible storage class.

Protocol Selection:The documentation explicitly mandates the creation of anNFS(Network File System) export (specifically NFS v4 is often recommended) on a Nutanix Files server to fulfill this RWX requirement.

Deployment Workflow:During the infrastructure setup, the administrator must create a storage class that dynamically or statically provisions these NFS shares. This allows multiple pods (such as inference servers and model managers) to access the heavy model files simultaneously across different nodes in the Kubernetes cluster.

Why not others:

Blockstorage (ReadWriteOnce) is used for internal components like the database (nai-db) and logging, but it cannot support the shared access required for the model repository.

CIFS(SMB) is not the standard protocol used for Kubernetes RWX Persistent Volumes in this specific NAI deployment workflow.

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

When deploying Nutanix Enterprise AI on managed Kubernetes services like Google Kubernetes Engine (GKE) or Azure Kubernetes Service (AKS), the management of GPU drivers is typically handled by theNVIDIA GPU Operatoror the cloud provider's pre-configured node images.

Driver Management:The deployment workflow involves installing the NVIDIA GPU Operator, which automatically manages the driver container, container toolkit, and device plugin.

Manual Steps:Consequently, the administrator shouldSkip installing the GPU drivermanually on the worker nodes during the preparation phase. Installing drivers manually can create conflicts with the drivers deployed by the GPU Operator or the underlying managed OS image.