Validates the ability to build and deploy GenAI applications on the Databricks Intelligence Platform.
Answer : It serves as the reliable, high-performance base for storing both original documents and their corresponding vector embeddings.
Provides ACID transactions and scalability for both batch and real-time AI workflows.
Answer : A feature that allows calling LLMs directly from SQL queries using functions like `ai_query()`.
Enables analysts to perform semantic analysis and text generation at scale without leaving the SQL environment.
Answer : The process of measuring model quality using metrics like faithfullness, relevance, and toxicity, often using MLflow and Lakehouse Monitoring.
Crucial for ensuring production models are safe and useful for the business.
Answer : A managed service that allows you to take base models and train them on your private organizational data to improve accuracy for specific tasks.
Automates the complex infrastructure needed for distributed deep learning operations.
Answer : A system optimized for storing and querying multi-dimensional vectors (embeddings) to support fast semantic search.
Enables the 'Retrieval' part of the RAG architecture.