Databricks Certified Generative AI Engineer - Associate
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A Generative AI Engineer is developing an internal chatbot that must classify user questions and route them to the appropriate models. For instance, a user might inquire about the historical failure rates of a specific electrical part, while another might ask for troubleshooting steps for a piece of electrical equipment. The available data sources are a database of PDF manuals for electrical equipment and a table containing failure records for electrical parts.
Which workflow should be implemented to support this chatbot?
A
Parse the electrical equipment PDF manuals into a table of question and response pairs. That way, the same chatbot can query tables easily to answer questions about both historical failure rates and equipment troubleshooting.
B
The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s a historical failure rate question, send the query to a text-to-SQL model. If it’s a troubleshooting question, then send the query to another model that summarizes the equipment-specific document and generates the response.
C
There should be two different chatbots handling different types of user queries.
D
The table with electrical part failures should be converted into a text document first. That way, the same chatbot can use the same document retrieval process to generate answers regardless of question types.