Microsoft Certified: Fabric Analytics Engineer AssociateImplement and manage semantic models (30-35%)Hard
A company has a requirement to refresh its semantic model in Microsoft Fabric daily at 3:00 AM UTC. Additionally, if any of the underlying source data (from a Data Lakehouse) changes outside of this schedule, the semantic model should be refreshed immediately. How should the refresh strategy be configured?
- ASet up a scheduled refresh for 3:00 AM UTC and manually trigger refreshes when source data changes.
- BConfigure a scheduled refresh for 3:00 AM UTC and enable dataset refresh via the Power BI REST API for immediate updates.
- CSet the scheduled refresh to run every 5 minutes to ensure minimal latency, covering both daily and immediate needs.
- DUse a scheduled refresh for 3:00 AM UTC and configure a webhook or Azure Function to trigger a refresh via the Power BI REST API upon data changes in the Data Lakehouse.
Show answer & explanationAnswer & explanation
Correct answer: D. Use a scheduled refresh for 3:00 AM UTC and configure a webhook or Azure Function to trigger a refresh via the Power BI REST API upon data changes in the Data Lakehouse.
To meet both scheduled and event-driven refresh requirements, a combination of scheduled refresh for the daily update and a programmatic trigger (e.g., webhook or Azure Function calling the Power BI REST API) upon source data changes is the most efficient and robust solution.
Why the other options are wrong
- A. Manually triggering refreshes is not scalable or reliable for immediate, event-driven requirements.
- B. Enabling the REST API is part of the solution, but it doesn't automatically *trigger* the refresh; an external mechanism is needed to call it.
- C. Refreshing every 5 minutes is inefficient, consumes excessive resources, and may not be feasible for very large models, especially if data changes are infrequent.
Event-Driven Refresh
A semantic model refresh strategy where the refresh process is initiated automatically in response to specific events, such as source data changes, rather than on a fixed schedule.
- Achieved using Power BI REST APIs.
- Often integrated with Azure Functions, Logic Apps, or webhooks.
- Ensures data freshness upon source updates, reduces unnecessary refreshes.
Memory trick: Scheduled for routine, API for events, Incremental for big data.