Microsoft Certified: Fabric Analytics Engineer AssociateImplement and manage semantic models (30-35%)Easy
A data engineer is managing refresh operations for a large semantic model in Microsoft Fabric. The model contains a fact table with billions of rows, partitioned by month. Due to the size, a full refresh takes an unacceptably long time. The requirement is to efficiently refresh only the most recent 12 months of data, while historical data remains static. Which refresh strategy should the engineer configure?
- AFull refresh with a data refresh timeout.
- BDirectQuery storage mode for the fact table.
- CScheduled refresh every 12 months.
- DIncremental refresh policy.
Show answer & explanationAnswer & explanation
Correct answer: D. Incremental refresh policy.
Incremental refresh is designed for this exact scenario: it allows you to define a policy to refresh only a subset of the data (e.g., the last N months) while retaining older, static data, significantly reducing refresh times for large tables.
Why the other options are wrong
- A. A full refresh is what the engineer is trying to avoid due to its long duration.
- B. DirectQuery doesn't involve refreshing data as it queries the source live, which might not be suitable if in-memory performance is also desired.
- C. Scheduling a full refresh every 12 months would still lead to a very long refresh process and wouldn't provide up-to-date data for the recent period.
Incremental Refresh
A refresh strategy for Import mode semantic models that selectively refreshes only the most recent data partitions, significantly reducing refresh times for large, growing tables.
- Requires defining RangeStart and RangeEnd parameters in Power Query.
- Optimizes refresh for large fact tables.
- Retains historical data while updating new data.
Memory trick: Incremental refresh: just add the new, keep the old.