Explanation: Approximate nearest neighbor (ANN) indexing enables efficient retrieval by optimizing similarity search. Metadata filtering does not optimize embeddings, numerical representations lose meaning, and sequential parsing adds latency. Which Oracle AI Vector Search feature improves retrieval effectiveness in a RAG workflow?
for data and queries to ensure accurate results, a principle that applies universally, even outside the database. Reference: Oracle Database 23ai AI Vector Search Guide, Section on Vector Embedding Consistency. You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?
preserve data integrity. CTAS (A) is permitted, as it copies the VECTOR column intact into a new table, maintaining its structure. Dropping a VECTOR column (B) is allowed via ALTER TABLE DROP COLUMN, as it simply removes the column without altering its type. Adding a new VECTOR column (D) is supported with ALTER TABLE ADD, enabling schema evolution. However, modifying an existing VECTOR column’s data type to a non -VECTOR type (C) (e.g., VARCHAR2, NUMBER) is not permitted because VECTOR is a specialized type with dimensional and format constraints (e.g., FLOAT32), and Oracle does not support direct type conversion due to potential loss of semantic meaning and structure. This restriction is documented in Oracle’s SQL reference. Reference: Oracle Database 23ai SQL Language Reference, Section on VECTOR Data Type Restrictions. Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?
Explanation: IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re -indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn’t directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle’s IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism. Reference: Oracle Database 23ai AI Vector Search Guide, Section on IVF Index Tuning. What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?
Explanation: Oracle Database 23ai introduces DBMS_AI as the primary PL/SQL package for interacting with Generative AI services, such as OCI Generative AI, enabling features like natural language query processing (e.g., Select AI) and AI -driven insights. DBMS_ML (B) focuses on machine learning model training and management, not generative AI. DBMS_VECTOR_CHAIN (C) supports vector processing workflows (e.g., document chunking, embedding), but it’s not the main interface for generative AI services. DBMS_GENAI (D) is not a recognized package in 23ai documentation. DBMS_AI’s role is highlighted in Oracle’s AI integration features for 23ai. Reference: Oracle Database 23ai New Features Guide, Chapter on AI Integration. Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?
➢ TOTAL QUESTIONS: 360 Which Oracle feature enhances performance when generating vector embeddings at scale?
