Vector Database Management

• Identify the core purpose of vector representations in modern computing systems

• Distinguish between structured data and unstructured data in database environments

• Analyze why traditional databases struggle with high-dimensional search queries

• Explain how machine learning models generate numerical vector representations

• Calculate distance between vectors using common geometric measurement techniques

• Compare different indexing strategies for efficient vector data retrieval

• Describe the lifecycle of a single vector database search query

• Demonstrate understanding of Hierarchical Navigable Small World graphs

• Summarize how data compression reduces memory usage in vector databases

• Identify challenges associated with scaling vector databases across multiple servers

• Design a simple recommendation engine using vector similarity search

• Implement a basic image search pipeline using vector embeddings

• Integrate vector databases with large language models for context

• Evaluate performance metrics to tune a production vector database

• Predict the evolution of vector database technology in the next decade