Microsoft Certified: Fabric Analytics Engineer AssociateImplement and manage semantic models (30-35%)Medium
A data engineer is developing a semantic model in Microsoft Fabric. The model will consume data from several large tables in a data warehouse. To optimize query performance for frequently accessed aggregations (e.g., total sales by month), the engineer wants to pre-calculate and store these summary tables within the semantic model. Which feature should the engineer implement?
- AImplement automatic aggregations.
- BUse DirectQuery for all tables.
- CCreate calculated tables using DAX.
- DConfigure incremental refresh for the fact tables.
Show answer & explanationAnswer & explanation
Correct answer: A. Implement automatic aggregations.
Automatic aggregations in Microsoft Fabric (and Power BI) allow you to define aggregation tables that the query engine can use automatically to answer queries, significantly improving performance for common aggregation scenarios without manual management of calculated tables.
Why the other options are wrong
- B. DirectQuery retrieves data directly from the source, which can be slow for complex aggregations, contradicting the goal of pre-calculation.
- C. Calculated tables can store aggregations, but automatic aggregations offer smarter query redirection and management.
- D. Incremental refresh optimizes data loading for large tables but doesn't directly address query performance for aggregations.
Automatic Aggregations
A feature in Fabric semantic models that allows the creation of pre-summarized tables that the query engine can automatically use to answer queries, improving performance.
- Improves query performance for common aggregations.
- The engine automatically redirects queries to the most appropriate aggregation table.
- Can be configured as Import, DirectQuery, or Dual storage mode.
Memory trick: Aggregations automatically speed up your summaries.