Microsoft Certified: Fabric Analytics Engineer AssociateImplement and manage semantic models (30-35%)Medium
A data engineer is designing a semantic model in Microsoft Fabric. The model needs to track daily inventory levels and sales. The inventory data is updated once a day, and sales data is updated every hour. The model should provide the most up-to-date sales figures while maintaining daily inventory snapshots. What is the most efficient way to configure the refresh strategy for the semantic model components?
- AUse incremental refresh for both inventory and sales data.
- BConfigure separate refresh schedules for the 'Inventory' table (daily) and the 'Sales' table (hourly) within the same semantic model.
- CConfigure two separate semantic models, one for inventory and one for sales, each with its own refresh schedule.
- DSet a single daily refresh for the entire semantic model.
Show answer & explanationAnswer & explanation
Correct answer: B. Configure separate refresh schedules for the 'Inventory' table (daily) and the 'Sales' table (hourly) within the same semantic model.
Microsoft Fabric allows configuring separate refresh schedules for individual tables within a semantic model. This enables the data engineer to refresh the 'Sales' table hourly for up-to-date figures and the 'Inventory' table daily for its snapshot nature, optimizing resource usage and data freshness.
Why the other options are wrong
- A. Incremental refresh is for refreshing only new/changed data, which is good for large tables, but it doesn't intrinsically solve the problem of differing refresh frequencies for different tables without separate schedules.
- C. Creating two separate semantic models would introduce unnecessary complexity and potential overhead for cross-analysis, as they might need to be joined or related in reports.
- D. A single daily refresh for the entire model would mean sales data is not up-to-date every hour, failing the requirement.
Table-level Refresh Schedule
Microsoft Fabric allows setting individual refresh schedules for tables within a semantic model, enabling different data freshness requirements for different data sources or components.
- Optimizes refresh cycles and resource consumption.
- Ensures specific tables are refreshed at their required frequency.
- Maintains a single, cohesive semantic model.
Memory trick: Each table has its own clock, sales tick fast, inventory stock.