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Exam Certified Generative AI Engineer Associate topic 1 question 68 discussion

Actual exam question from Databricks's Certified Generative AI Engineer Associate
Question #: 68
Topic #: 1
[All Certified Generative AI Engineer Associate Questions]

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?

  • 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.
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Suggested Answer: A 🗳️

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seaun
1 week, 6 days ago
Selected Answer: A
Feature Serving Endpoint with Online Store: This approach is specifically designed for providing low-latency access to feature data in real-time. The cinema's continuously updated showtime data can be synced from the Delta table to an online store, such as the Databricks Feature Store's online store.
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Duke_CT
2 weeks ago
Selected Answer: D
Hmm, seems like A from what I studied.
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