The correct Oracle AI Vector Search design is a single SQL query combining the relational city restriction with vector-distance ranking . The uploaded source explicitly identifies option C. Oracle AI Database's native VECTOR type enables vector similarity searches without moving business information to a separate vector database. Oracle specifically states that vector searches can be combined with sophisticated business-data searches using SQL and the capabilities of its converged database.

The recruiting system can store an embedding of each job description alongside ordinary relational attributes such as city, employer, salary, employment type, and status. The candidate's resume or search request is converted into a query vector. SQL can then apply a predicate such as city = :candidate_city, calculate vector distance against qualifying job embeddings, order the result by that distance, and return the top matches.

Exporting postings to another vector platform unnecessarily introduces data movement and loses the direct integration with current relational attributes. Graph edge labels are intended for relationship modeling, not semantic document representation. “Early maintenance” is unrelated to vector ranking.

The key Oracle AI Database principle being tested is semantic vector retrieval and conventional relational business filtering within one SQL operation .

Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database — vector-distance ordering, relational predicates, top-K retrieval, and converged SQL.