Authentic Oracle Certification Exam 1z0-184-25 Questions Answers

Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn’t required; VECTOR is a distinct type. Data Pump doesn’t treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle’s Data Pump documentation confirms seamless handling of VECTOR data.

SQLLoader in Oracle 23ai supports loading VECTOR data from CSV files, requiring vectors to be formatted as text. A critical consideration is enclosing components in curly braces (A), e.g., {1.2, 3.4, 5.6}, to match the VECTOR type’s expected syntax (parsed into FLOAT32, etc.). FVEC (B) is a binary format, not compatible with CSV text input; SQLLoader expects readable text, not fixed offsets. Sparse format (C) isn’t supported for VECTOR columns, which require dense arrays. SQLLoader doesn’t normalize vectors automatically (D); formatting must be explicit. Oracle’s documentation specifies curly braces for CSV-loaded vectors.

RAG in Oracle AI Vector Search integrates vector search with LLMs, leveraging database-stored data. A key advantage is its use of existing database security and access controls (D), ensuring that sensitive enterprise data remains secure while being accessible to LLMs, aligning with Oracle’s security model (e.g., roles, privileges). Performance optimization (A) occurs but isn’t the primary focus; storage increases are minimal compared to security benefits. Real-time extraction (B) is possible but not RAG’s core strength, which lies in static data augmentation. Training LLMs (C) is unrelated to RAG, which uses pre-trained models. Oracle emphasizes security integration as a standout RAG feature.