An insurance company is building a star-schema warehouse to analyze claim payments. The Claims_Fact table must keep one row per claim that stores the total amount paid. Because a single claim may involve several adjusters-and each adjuster can work on many claims-business users also need to filter and group claim costs by adjuster without inflating the overall totals.
Which modeling technique will best satisfy these requirements while preserving the one-row-per-claim grain of the fact table?
Store a comma-separated list of all AdjusterIDs in a single column inside Claims_Fact.
Create a bridge (association) table that lists each ClaimID-AdjusterID combination and join it between Claims_Fact and the Adjuster dimension.
Snowflake the Adjuster dimension into master-detail tables and join the master table directly to Claims_Fact.
Insert a duplicate row in Claims_Fact for every adjuster on the claim and divide the paid amount equally among the duplicates.
A bridge (association) table resolves the many-to-many relationship between Claims_Fact and the Adjuster dimension by storing one row for every ClaimID-AdjusterID pair. Two one-to-many joins (Claims_Fact → Bridge and Bridge → Dim_Adjuster) let reports slice paid amounts by adjuster, yet the fact table keeps a single row per claim so totals do not double-count. Snowflaking the dimension, concatenating keys in the fact table, or duplicating fact rows either breaks star-schema simplicity, complicates queries, or risks inaccurate aggregations.
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CompTIA Data+ DA0-002 (V2)
Data Concepts and Environments
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