A central data team in Microsoft Fabric keeps curated dimension tables in a Warehouse and large raw event Delta tables in a separate Lakehouse in the same workspace. An analyst must run a single T-SQL query that joins the Warehouse dimensions to the Lakehouse event tables and writes the joined result into a managed table for nightly reporting. The team works only in T-SQL and wants one store to author and persist this cross-item query. Which data store should host the query and its output table?
- AA Microsoft Fabric Lakehouse queried through its SQL analytics endpoint, because the endpoint can join the Warehouse tables and persist the joined managed table from the same T-SQL session.
- BA Microsoft Fabric Warehouse, because its T-SQL engine can issue a cross-database query that joins its own tables to the Lakehouse SQL analytics endpoint and then write the joined result into a managed table. Correct
- CA Microsoft Fabric Lakehouse written by a PySpark notebook, because Spark can read the Warehouse and Lakehouse tables and persist the joined result as a managed Delta table for reporting.
- DA Microsoft Fabric Eventhouse with a KQL database, because a KQL cross-cluster query joins the Warehouse and Lakehouse tables and materialises the joined result for reporting.
Why A is wrong: The Lakehouse SQL analytics endpoint is read-only, so although it can reference other items it cannot persist a managed table from the joined result, which means it fails the write half of the requirement.
Why B is correct: The Warehouse T-SQL engine supports cross-database queries that reference a Lakehouse SQL analytics endpoint in the same workspace, and it is a full read-write store, so it can both join across items and persist the managed output table.
Why C is wrong: Spark could combine the data, but the team works only in T-SQL and asked for a single T-SQL query, so introducing a PySpark notebook changes the engine rather than meeting the stated skills and authoring requirement.
Why D is wrong: An Eventhouse targets append-only telemetry queried in KQL, not T-SQL joins across a Warehouse and a Lakehouse, so it neither matches the team skills nor serves a curated cross-item reporting join.