Given the following items: Name | Type ---|--- Notebook1 | Notebook Notebook2 | Notebook Lakehouse1 | Lakehouse Pipeline1 | Data pipeline Model1 | Semantic model Note: For Model1, the Keep your…

This question tests knowledge of Microsoft Fabric orchestration mechanisms - specifically how to trigger workloads via time-based schedules, storage events, and success-based dependencies across notebooks, pipelines, and semantic models.

Implement and manage an analytics solution

Question

Given the following items:
NameType
Notebook1Notebook
Notebook2Notebook
Lakehouse1Lakehouse
Pipeline1Data pipeline
Model1Semantic model

Note: 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.

Name	Type
Notebook1	Notebook
Notebook2	Notebook
Lakehouse1	Lakehouse
Pipeline1	Data pipeline
Model1	Semantic model

Explanation

Approach. Notebook1 should be orchestrated via a Data Pipeline with a scheduled trigger set to recur on weekdays at 8:00 AM; within that same pipeline, a Semantic Model refresh activity for Model1 is chained after Notebook1 with an 'On success' dependency condition - this is necessary because Model1 has 'Keep your Direct Lake data up to date' disabled, meaning it will NOT auto-refresh when the lakehouse data changes and must be explicitly triggered. Notebook2 must be placed inside a separate Data Pipeline configured with a Storage Event Trigger pointing to the Azure Blob Storage container, because file-arrival events from blob storage can only be captured at the pipeline level, not directly on a standalone notebook. The lakehouse (Lakehouse1) and the semantic model (Model1) do not require their own independent schedules here - they are downstream consumers driven by the pipeline activities described above.

Concept tested. Microsoft Fabric workload orchestration: distinguishing between (1) scheduled triggers on Data Pipelines for time-based execution, (2) Storage Event Triggers on Data Pipelines for blob-arrival-based execution, and (3) activity-level success dependencies within a pipeline to chain a Semantic Model refresh after a notebook completes - particularly when Direct Lake auto-refresh is disabled on the model.

Reference. Microsoft Learn - Schedule and orchestrate notebooks and pipelines in Microsoft Fabric; Fabric Data Pipeline triggers (scheduled vs. event-based); Direct Lake semantic model refresh behavior

Topics

#Data Orchestration#Fabric Data Pipelines#Scheduled Triggers#Event Triggers

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