Question
You have a Fabric workspace named Workspace1 that contains the items shown in the following table.
| Name | Type |
|---|---|
| Notebook1 | Notebook |
| Notebook2 | Notebook |
| Lakehouse1 | Lakehouse |
| Pipeline1 | Data pipeline |
| Model1 | Semantic model |
For Model1, the Keep your Direct Lake data up to date option is disabled.
You need to configure the execution of the items to meet the following requirements:
- Notebook1 must execute every weekday at 8:00 AM.
- Notebook2 must execute when a file is saved to an Azure Blob Storage container.
- Model1 must refresh when Notebook1 has executed successfully.
How should you orchestrate each item? To answer, select the appropriate options in the answer area.
Explanation
Notebook1: add it to Pipeline1 so the pipeline can run it on a schedule and chain further work after it succeeds; a Spark job definition runs Spark code files, not notebooks. Notebook2: Azure Blob Storage events are available in Real-Time hub, where an Activator alert on a blob-created event can run the notebook, so the trigger is configured from Real-Time hub. Pipeline1: give the pipeline a schedule for weekdays at 8:00 AM; that runs Notebook1 at the required time. Model1: add a semantic model refresh activity to Pipeline1 after the Notebook1 activity on its success path, so the model refreshes only when Notebook1 succeeds. Turning on Keep your Direct Lake data up to date would refresh on any data change, not specifically after Notebook1 succeeds.
