A Microsoft Fabric medallion solution lands raw source extracts in a bronze Lakehouse layer and applies cleansing, deduplication, and type conformance in a silver layer. The team now needs to add the star schema for Power BI reporting, with surrogate keys, Type 2 dimension history, and fact tables built at a defined grain. In which layer of the medallion architecture should this dimensional model be built?
- AIn the bronze layer, alongside the raw extracts, so the dimensional model sits as close to the original ingested data as possible and avoids an extra transformation hop.
- BIn the silver layer, replacing the cleansed tables, because once data is cleansed it should immediately be reshaped into facts and dimensions to avoid maintaining a separate layer.
- CSplit across silver and gold, keeping dimensions in silver and facts in gold, because dimensions change slowly and facts are query-facing while dimensions are not.
- DIn the gold layer, built from the cleansed silver tables, because gold is where business-ready, query-optimised star schemas with surrogate keys and conformed dimensions belong. Correct
Why A is wrong: Bronze holds raw, unmodelled data exactly as ingested; building surrogate keys and Type 2 history there mixes raw landing with curated modelling and defeats the layering the medallion architecture exists to provide.
Why B is wrong: Silver is for cleansed, conformed but still largely source-shaped tables; overwriting it with the star schema collapses two distinct responsibilities and removes the reusable cleansed layer that multiple gold models may share.
Why C is wrong: Facts and their dimensions form one consumption model and must sit together so surrogate keys line up; placing dimensions in silver and facts in gold separates the star schema across layers and breaks the clean consumption boundary.
Why D is correct: The gold layer holds business-ready, consumption-optimised models, so the star schema with surrogate keys, Type 2 dimensions, and defined-grain facts is built there from cleansed silver data, ready for Power BI reporting.