Question

A GenAI engineer deployed a RAG chatbot to a Model Serving endpoint two weeks ago. Leadership now wants a Lakehouse Monitoring dashboard that tracks request volume, latency trends, and response quality drift starting from launch day. When the engineer opens the endpoint, they discover no inference table exists and no payload logs were captured. What is the correct conclusion about building the requested historical dashboard?

Accepted Answer

Historical data from launch cannot be recovered; inference tables only capture payloads logged after the feature is enabled on the endpoint, so monitoring can only begin going forward once logging is turned on.

Answer

Lakehouse Monitoring can automatically backfill the inference table by replaying all historical requests stored in the endpoint's internal cache.

Answer

The engineer should enable MLflow autologging retroactively, which will reconstruct the two weeks of missing request and response payloads.

Answer

Because the endpoint already served traffic, the inference table exists by default and simply needs to be attached to a Lakehouse Monitoring profile.