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When analyzing user behavior data with potentially hundreds of dimensions in a lakehouse architecture, which data modeling strategy would best optimize for query performance and scalability?
A
Storing user events in a single wide table with a column for each dimension to minimize join operations
B
Creating a pre-aggregated summary table that updates daily with the most common analytics queries
C
Utilizing a star schema with dimension tables for user attributes and a fact table for behavior events
D
Implementing a snowflake schema where user behavior events are normalized into multiple related tables
