Databricks Certified Generative AI Engineer Associate — Question 71

Generative AI Engineer is helping a cinema extend its website’s chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability in their RAG application.

Which option will do this with the least effort and in the most performant way?

Answer options

• 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.

Correct answer: A

Explanation

Option A is the best choice because it allows for efficient querying of the most up-to-date showtime information through a Feature Serving Endpoint, minimizing latency and reducing the need for complex SQL queries. Option B, while possible, may introduce delays and inefficiencies due to real-time SQL query construction. Option C adds unnecessary complexity by involving an external database, which could lead to data staleness. Option D involves additional steps for embedding and indexing, making it less efficient compared to the direct querying provided by Option A.