Databricks Certified Generative AI Engineer - Associate
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Ultimate access to all questions.
A Generative AI Engineer is enhancing a cinema's website chatbot to answer questions about specific movie showtimes at the user's local theater. The user's location is already provided to the agent, and a continuously updated Delta table contains the latest showtime information by location. They want to implement this new capability in their RAG application.
Which approach accomplishes this with the least effort and the highest performance?
A
Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation.
B
Query the Delta table directly via a SQL query constructed from the user’s input using a text-to-SQL LLM in the agent logic / tool implementation.
C
Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation.
D
Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation.