Question

A GenAI engineer has built a RAG chain and wants to test it against a batch of 500 evaluation questions before deploying to a Model Serving endpoint. They log the chain with MLflow using the langchain flavor. After registering the model in Unity Catalog, they want to validate that the served model will accept the same request format they plan to send from a downstream client. Which action provides the strongest guarantee that the deployed endpoint schema matches what the client will send?

Accepted Answer

Provide an input_example when logging the model so MLflow infers and stores a model signature that Model Serving uses to validate incoming requests

Answer

Set scale-to-zero on the endpoint so the first request warms up the container and reveals any schema issues

Answer

Increase the endpoint's provisioned concurrency to ensure all 500 batch requests are processed simultaneously

Answer

Enable inference tables on the endpoint to log every request payload after deployment