Databricks Certified Generative AI Engineer Associate — Question 12

A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. The match should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.
How should the Generative Al Engineer architect their system?

Answer options

• A. Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.
• B. Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members’ profiles and perform keyword matching to find the best available team member.
• C. Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.
• D. Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.

Correct answer: D

Explanation

Option D is correct because it utilizes a vector store to embed team profiles, allowing effective retrieval based on the project scope, which aligns with the requirement for matching profiles with unstructured text. Options A and C focus on retrieval but do not incorporate the advantages of vector embeddings as effectively as D. Option B, while using keyword extraction, does not leverage the full potential of unstructured data matching through embeddings.