An analytics engineer in "Microsoft Fabric" wants to author, save, and share a set of exploratory KQL queries that run against tables in an "Eventhouse", keeping the queries grouped as reusable tabs alongside the data. Which Fabric item is designed to hold and run these saved KQL queries?
- AA KQL queryset, which stores multiple named KQL queries as tabs that run against Eventhouse and KQL database tables and can be saved and shared as a Fabric item. Correct
- BA "Dataflows Gen2" item, which authors the reusable transformations and saves them as steps that run against the Eventhouse tables for the exploratory analysis required.
- CA "SQL analytics endpoint", which stores the reusable query definitions as views that run against the Eventhouse tables and can be saved and shared as a Fabric item.
- DA "semantic model", which stores the reusable query definitions as measures that run against the Eventhouse tables and can be saved and shared as a Fabric item.
Why A is correct: A KQL queryset is the Fabric item purpose-built to author, organise into tabs, save, and share KQL queries that run directly against Eventhouse and KQL database data.
Why B is wrong: Dataflows Gen2 uses Power Query for ingestion and transformation, not KQL authoring, so it is the wrong surface for writing and saving Kusto queries against an Eventhouse.
Why C is wrong: A SQL analytics endpoint exposes a read-only T-SQL surface over Lakehouse tables, not a KQL authoring workspace, so it cannot hold tabbed Kusto querysets.
Why D is wrong: A semantic model defines DAX measures and relationships for reporting, not saved KQL queries, so it does not serve as the authoring surface for Kusto exploration.