Study with NCP-AI most valid questions & verified answers

A development team is building a new AI-powered customer service chatbot. They have successfully integrated an LLM from Hugging Face into their application via a Nutanix Enterprise AI (NAI) endpoint. The NAI administrator has confirmed the endpoint is in the 'Active' state, and internal sample requests run from the NAI Endpoint section return an 'accurate' response and 'Succeeded' status.
However, the developers report that the chatbot frequently provides vague or irrelevant answers to user queries, leading to low customer satisfaction scores. They describe the LLM's performance for this specific chatbot task as "suboptimal." What should the administrator advise the developers to do next to improve the quality of the LLM's responses for the customer service chatbot task?

• A. Reseed the initial dataset and reimport the existing LLM within Nutanix Enterprise AI without implementing a RAG platform.
• B. Consult the Hugging Face documentation for the specific LLM to review its training data, intended use cases, and capabilities.
• C. Instruct the administrator to check the Kubernetes pod logs for the endpoint to identify any CRASHLOOPBACKOFF or OOMKilled errors that might be affecting model inference.
• D. Increase the number of instances for the NAI endpoint to enhance performance and reduce latency.

An AI/ML Admin initiates an import of a new Large Language Model (LLM) from Hugging Face using its Model URL into Nutanix Enterprise AI. After the import process begins, the LLM's status on the Models page changes to Pending.
Based on this status, what is the most probable cause for the delay?

• A. The NVIDIA GPU Operator is not installed on the Kubernetes cluster.
• B. The system is awaiting the necessary cluster resources.
• C. The Hugging Face access token provided is unauthorizeD.
• D. The LLM is a custom model that Nutanix has not validateD.

An administrator is deploying a customer support chatbot that needs to integrate with a large language model hosted in the Nutanix Enterprise AI platform. The goal is to enable real-time inference while following best practices for security and scalability.
Which action should the administrator perform to meet these requirements?

• A. Register the chatbot application in Prism Central and map it to the deployed model through application profiles.
• B. Create an API endpoint in the NAI Dashboard and generate an API key to assign to the endpoint for secure access.
• C. Enable access to the model through SSH and use raw socket communication between the chatbot and model service.
• D. Configure a firewall exception using Nutanix Flow and force restricted access from the chatbot subnet to the AI model.

An administrator has been requested by a developer team to provide them with an endpoint.
Which action must the administrator ensure is taken before creating the first endpoint?

• A. Create a new Nutanix Enterprise AI instance, and ensure that status is Running.
• B. Create a new Nutanix Enterprise AI instance, and ensure that status is CompleteD.
• C. Import a Large Language Model (LLM) to Nutanix Enterprise AI and ensure that the import status is Active.
• D. Import a Large Language Model (LLM) to Nutanix Enterprise AI and ensure that the import status is UploadeD.

A developer with experience using Open AI compatible clients is exploring using OpenAI Compatible endpoints within Nutanix Enterprise AI.
An administrator has provided the developer with a list of OpenAI compatible endpoints that Nutanix Enterprise AI supports but one of them is unsupporteD.
Which option shows the unsupported endpoint type?

• A. /v1/chat/completions
• B. /v1/systems
• C. /v1/models
• D. /v1/embeddings