Practice Exam

Explanations for each answer:

• Add Notebook1 to an Apache Spark job definition is incorrect. While you can create Apache Spark job definitions to run notebooks, this approach doesn't provide the orchestration needed to refresh Model1 after Notebook1 completes successfully. Spark job definitions execute independently and cannot chain semantic model refresh activities.
• Add Pipeline1 to an Apache Spark job definition is incorrect. Apache Spark job definitions are designed to run Spark applications and notebooks, not data pipelines. You cannot add a data pipeline to a Spark job definition. Pipelines and Spark job definitions are separate Fabric item types with different purposes.
• Add an Execute Notebook1 activity to Pipeline1 is correct. Adding Execute Notebook1 activity to Pipeline1 allows you to orchestrate the notebook execution within a pipeline. You can then schedule Pipeline1 to run every weekday at 8:00 AM, and chain additional activities (like refreshing Model1) to execute after the notebook completes successfully using OnSuccess dependencies.
• Add an Execute Notebook2 activity to Pipeline1 is incorrect. Adding Notebook2 to Pipeline1 would run it on a schedule, but the requirement is to execute Notebook2 when a file is saved to Azure Blob Storage. This is an event-driven scenario that requires Real-Time Hub event configuration, not scheduled pipeline execution.
• Add a Refresh Model1 activity to Pipeline1 is correct. The Semantic model refresh activity allows you to refresh Power BI semantic models from a pipeline. By adding this activity to Pipeline1 with an OnSuccess dependency on the Execute Notebook1 activity, Model1 will automatically refresh after Notebook1 completes successfully. This works for both Import and Direct Lake mode semantic models.
• From Real-Time hub, configure the execution of Notebook2 is correct. Real-Time Hub supports event-driven automation for Fabric items. You can subscribe to Azure Blob Storage events (such as blob created or updated) and configure an action to run Notebook2 when files are saved to the storage container. This enables near real-time execution based on storage events rather than schedules.
• From Real-Time hub, configure the execution of Pipeline1 is incorrect. Configuring Pipeline1 execution from Real-Time Hub would make the pipeline run in response to events rather than on the required weekday schedule. The requirement is for Notebook1 (via Pipeline1) to run every weekday at 8:00 AM, which is a scheduled execution, not an event-driven one.
• For Model1, enable the Keep your Direct Lake data up to date option is incorrect. While enabling this option would automatically update Model1 when lakehouse data changes, the requirement specifies that Model1 must refresh when Notebook1 has executed successfully, not when data changes. This creates a specific dependency that requires orchestration through a pipeline refresh activity rather than automatic updates.