Microsoft Certified: Fabric Analytics Engineer AssociateImplement and manage semantic models (30-35%)Medium
A data engineer is managing a large semantic model in Microsoft Fabric. The model sources data from an Azure Data Lake Storage Gen2 (ADLS Gen2) account, specifically from Parquet files. The data volume is growing rapidly, and a full refresh takes several hours daily, exceeding the allowable refresh window. The business requires data to be refreshed daily, but only the most recent data (last 30 days) needs to be frequently updated, while older data (beyond 30 days) can be refreshed less often or remain static. Which refresh strategy should be implemented?
- ASet up a standard scheduled refresh for the entire model once a day.
- BConfigure incremental refresh with a refresh policy for the last 30 days.
- CImplement an event-driven refresh triggered after all Parquet files are updated.
- DChange the storage mode of the entire model to DirectQuery.
Show answer & explanationAnswer & explanation
Correct answer: B. Configure incremental refresh with a refresh policy for the last 30 days.
Incremental refresh is designed for large tables in Import mode, allowing only a portion of the data (e.g., the last 30 days) to be refreshed frequently, while historical data remains static or refreshes less often. This drastically reduces refresh times while maintaining data freshness for recent periods.
Why the other options are wrong
- A. A standard full refresh takes too long, exceeding the refresh window.
- C. Event-driven refresh automates the trigger but doesn't solve the core problem of a long-running full refresh for a large Import mode model.
- D. Changing to DirectQuery would avoid refresh times but would likely lead to slower query performance for a large model and might not be suitable for all analytical needs.
Incremental Refresh
A refresh policy for large Import mode semantic models that optimizes refresh times by processing only new or changed data partitions, rather than a full reload of the entire table.
- Significantly reduces refresh duration for large datasets.
- Requires defining RangeStart and RangeEnd parameters in Power Query.
- Allows different refresh frequencies for historical vs. recent data.
Memory trick: Incremental refresh, only recent data gets fresh.