➢ TOTAL QUESTIONS: 360 Which Oracle feature enhances performance when generating vector embeddings at scale?
How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?
C. It translates prompts into SQL queries for precise execution D. It clusters queries into topic -based AI search categories Explanation: Select AI works by converting natural language prompts into SQL queries, allowing precise execution in Oracle AI Vector Search. The other options involve AI -related techniques but do not represent the core integration of Select AI. Which Oracle feature improves the performance of queries on stored vector embeddings?
AI Vector Search?
L2 normalization ensures embeddings have consistent magnitudes, improving similarity accuracy. Expanding embeddings does not always improve performance. Removing common words is relevant for NLP preprocessing but does not enhance vector -based retrieval. Dynamic indexing is inefficient. What happens when a vector index is created on a column with high -dimensional embeddings?
Explanation: Disabling indexing before bulk updates prevents unnecessary recalculations, improving efficiency. Normalization is useful but not always required, JSON storage is not ideal for structured vector search, and foreign keys do not directly optimize vector updates. Which technique ensures efficient query performance when searching vector embeddings in Oracle AI Vector Search?
