GENERATIVE-AI-ENGINEER-ASSOCIATE Question #8: Real Exam Question with Answer & Explanation

Question

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?

Options

• ACreate a Feature Serving Endpoint from a FeatureSpec that references an online store synced
• BQuery the Delta table directly via a SQL query constructed from the user's input using a text-to-
• CSet up a task in Databricks Workflows to write the information in the Delta table periodically to an
• DWrite the Delta table contents to a text column, then embed those texts using an embedding

Explanation

Chosen option is the most efficient and performant option for implementing the requested feature with the least amount of effort. Creating a Feature Serving Endpoint linked to the Delta table allows the chatbot to access up-to-date showtime information dynamically and with low latency, without needing additional transformations or external databases. The Feature Serving Endpoint will allow for efficient querying based on the user’s location, making it easy to integrate with the agent’s logic. This approach is specifically designed for serving feature data in production environments with minimal overhead.

No community discussion yet for this question.