Practice Questions

What is the primary purpose of vector embeddings in Oracle AI Vector Search?

A. Encrypt data B. Represent data numerically for similarity comparison C. Compress database tables D. Replace SQL queries

Which distance metric is commonly used for semantic similarity?

A. Manhattan distance B. Cosine similarity C. Binary distance D. Hash distance

What is the role of HNSW indexing?

A. Data encryption B. Backup optimization C. Fast approximate nearest neighbor search D. Query logging

Which Oracle feature allows storing vector embeddings directly?

A. JSON columns B. VECTOR data type C. XML DB D. LOB storage only

What is a key benefit of combining SQL with vector search?

A. Eliminates need for indexes B. Enables hybrid queries (structured + semantic) C. Reduces storage requirements D. Removes need for AI models

Which scenario best fits vector search usage?

A. Sorting numeric data B. Searching similar documents by meaning C. Counting rows D. Updating indexes