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?
Answer(s): D
Before installation, what kind of Kubernetes StorageClass must be provisioned for model files in an NFS shares for persistent volumes?
Answer(s): B
An administrator observes that inference requests to Nutanix Enterprise AI are experiencing increased latency. After accessing the Infrastructure Usage and Health page, the administrator finds that the Service Health status is marked as Critical, and the GPU utilization on one node is consistently at 100%, while other nodes show moderate usage.What is the most appropriate next step to resolve the performance issue?
Answer(s): A
An administrator has installed and deployed NAI, but the UI is not launching.Which command should the administrator use to ensure that all pods are successfully deployed?
What is the correct endpoint PATH that is displayed in the NAI Dashboard?
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