Explanation: Granularity of vector chunk representations directly affects retrieval accuracy, as smaller chunks allow for more precise document section matching. AI ranking, vector compression, and indexing transformations impact efficiency but do not primarily define accuracy. What should be considered when importing vector data using Oracle Data Pump?
Which design choice improves RAG performance in Oracle AI Vector Search?
Explanation: Balancing the number of partitions and the number of partitions probed (nprobe) per query optimizes accuracy while maintaining efficiency. The other options do not directly control the accuracy -speed tradeoff in an IVF -based search. Which factor should be considered when choosing an embedding model for external vector generation?
B. The existing vector embeddings will be converted to JSON. C. The associated vector index will be automatically dropped. D. The similarity function will be reassigned to other fields. Explanation: Dropping a vector column automatically removes the associated index. Converting embeddings to JSON, reassigning similarity functions, or manually reconfiguring indexes does not occur. Which condition must be met when inserting vector embeddings into a table?
What is the primary limitation of using Euclidean distance for similarity search?
➢ TOTAL QUESTIONS: 360 Which Oracle feature enhances performance when generating vector embeddings at scale?
