Oracle AI Vector Search Professional Intermediate Practice Exam: Medium Difficulty 2025

A data scientist is implementing vector search for a product recommendation system. The embedding vectors have 768 dimensions and contain both positive and negative values. Which distance metric would be MOST appropriate when the direction of the vectors is more important than their magnitude?

An Oracle database administrator needs to create a vector index for a table containing 50 million product descriptions with 1536-dimensional embeddings. The application requires fast approximate search with acceptable trade-offs in accuracy. Which index type and configuration approach would provide the BEST balance between query performance and accuracy?

A development team is building a semantic search application using Oracle AI Vector Search. After creating embeddings for their documents, they notice that queries for similar concepts return unexpected results. Upon investigation, they discover that different embedding models were used for documents and queries. What is the PRIMARY reason this approach fails?

An e-commerce company has implemented vector search for their product catalog. They notice that search performance degrades significantly during peak hours when thousands of concurrent users perform similarity searches. Which combination of optimization strategies would MOST effectively improve performance?

A company is integrating Oracle AI Vector Search with their existing RAG (Retrieval-Augmented Generation) application. They need to retrieve the top 10 most relevant document chunks based on user queries, then pass these to an LLM. Which query pattern would be MOST appropriate for this use case?

An application architect needs to implement hybrid search that combines traditional keyword search with vector similarity search. The business requirement states that results must match certain keywords AND be semantically similar to the query. How should this be implemented in Oracle AI Vector Search?

A database administrator notices that vector search queries are returning results quickly for small result sets (k=10) but performance degrades exponentially when users request larger result sets (k=1000). The table contains 100 million vectors with an HNSW index. What is the MOST likely cause and solution?

A machine learning team wants to implement a document similarity search where users can upload a document and find similar historical documents. The documents vary significantly in length from 100 to 10,000 words. Which approach to generating and storing embeddings would be MOST effective?

An organization is implementing vector search for a multi-tenant SaaS application where each customer's data must be strictly isolated. They have 500 customers with varying data volumes (1,000 to 10 million vectors per customer). Which architecture approach provides the BEST balance of performance, isolation, and maintainability?

A development team observes that their vector search returns highly accurate results in development but significantly less accurate results in production, despite using identical queries and the same embedding model. The production dataset is 100x larger than development. What is the MOST likely cause and appropriate solution?