What happens when a chunk of text is vectorized?

The correct answer is A because vectorization converts text chunks into numerical embeddings that capture semantic meaning. In Agentforce and Data 360 retrieval scenarios, large documents are first broken into smaller chunks or passages, and those chunks are then converted into vectors. The retrieval engine can compare a user’s question or prompt with those vectors to find content that is meaningfully similar, not merely keyword-matched. Option B is wrong because vectorization is not encryption; security and storage controls are separate platform concerns. Option C is wrong because vectorization is not a compression mechanism and is not used primarily to reduce file size or storage cost. Salesforce Data 360 documentation describes chunking as creating manageable semantic units and vectorization as converting those chunks into numeric representations of text.