Oracle AI Vector Search Professional Practice Exam: Test Your Knowledge 2025

What is the primary purpose of vector embeddings in Oracle AI Vector Search?

A data scientist needs to store vector embeddings with 1536 dimensions in Oracle Database. Which data type should be used to store these vectors?

Which distance metric is most commonly used for normalized vector embeddings in semantic similarity searches?

An application performs vector similarity searches on a table with 10 million records. Which index type should be created to optimize query performance?

What is the main trade-off when using approximate nearest neighbor (ANN) algorithms versus exact nearest neighbor searches?

A company is building a semantic search application that needs to convert user queries into vector embeddings. Which component is responsible for this transformation?

When configuring an HNSW vector index in Oracle Database, which parameter controls the trade-off between index build time and search accuracy?

A vector search query is returning results but performance is degrading as the table grows beyond 50 million records. The vector index exists but queries are still slow. What is the most likely cause?

Your organization wants to implement a RAG (Retrieval-Augmented Generation) pattern using Oracle AI Vector Search. What is the primary role of vector search in this architecture?

When designing a vector search solution, you need to decide between storing vectors in a dedicated vector column versus storing them with associated metadata in JSON format. What is the recommended approach?

A vector search application needs to filter results based on metadata (e.g., document category, date range) before performing similarity search. Which approach provides the best performance?

You are implementing a multi-tenant SaaS application using Oracle AI Vector Search. Each tenant has their own set of documents. What is the best practice for data isolation while maintaining query performance?

What is the purpose of the TARGET_ACCURACY parameter when creating a vector index in Oracle Database?

An e-commerce company wants to implement visual product search where users can upload images to find similar products. Which components are required in this solution?

When should you consider re-generating vector embeddings for existing documents in your vector search system?

A financial services company needs to implement vector search on sensitive customer documents while ensuring data privacy. The documents must be searchable but the embedding process must not expose data to external services. Which architecture best meets these requirements?

You are optimizing a vector search query that combines similarity search with complex joins and aggregations. The query plan shows that the vector index is being accessed but overall performance is still poor. What is the most likely bottleneck?

Your application uses different embedding models for different data types: one for product descriptions (768 dimensions), another for user reviews (512 dimensions), and another for images (2048 dimensions). What is the recommended database design approach?

In a hybrid search scenario, you need to combine vector similarity search with traditional full-text search and rank results based on both semantic similarity and keyword relevance. What is the most effective approach in Oracle Database?

A global application serves users across different languages. You need to implement multilingual semantic search where users can search in their language and find relevant results regardless of document language. What is the recommended approach?