Manage Batches and Pipelines DP-203 Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   Which Azure Data Factory feature enables you to store pipeline definitions in a Git repository for version control?

   • AIntegration runtime
   • BTrigger
   • CGit integration
   • DData Flow
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2. 12expert · hard

   A company runs a batch pipeline in Azure Synapse that processes data from multiple sources. The pipeline is triggered by a schedule that runs every hour. The pipeline sometimes takes longer than an hour to complete, and the team has noticed that overlapping runs are causing data consistency issues. They want to prevent overlapping runs while still ensuring that every hour is processed, even if a run takes longer than an hour. What should they do?

   • AUse a schedule trigger and set the pipeline's concurrency limit to 1.
   • BUse a 'Until' activity to loop until the previous run is complete.
   • CAdd a 'Wait' activity at the beginning of the pipeline to delay the start until the previous run finishes.
   • DChange the trigger to a tumbling window trigger with a 1-hour window and set 'Max concurrency' to 1.
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4. 13expert · hard

   A company uses Azure Data Factory and has a Git repository configured for development. The team has multiple developers working on different pipelines. They want to ensure that changes made by developers are not published to the live Data Factory until they have been reviewed and approved. They also want to maintain a history of changes. What should they do?

   • AHave developers publish directly from their feature branches to the live Data Factory.
   • BUse a branch policy in Azure DevOps that requires a pull request for changes to be merged into the main branch, and then publish from the main branch.
   • CDisable Git integration and use the Data Factory UI's publish button after manual review.
   • DUse a separate Data Factory workspace for each developer and manually merge changes.
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5. 14foundation · easy

   Which Azure Data Factory feature allows you to group related pipelines and datasets into a single manageable unit?

   • AFactory
   • BFolder
   • CIntegration runtime
   • DTrigger
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6. 15expert · hard

   A financial company has a batch pipeline that loads daily trading data from a CSV file into Azure SQL Database. The pipeline uses a copy activity and then a stored procedure to update a summary table. The pipeline runs at 6:00 PM every day. One day, the pipeline fails during the stored procedure due to a deadlock. The company wants to ensure that the pipeline is automatically retried and that the summary table is not left in an inconsistent state. What should you do?

   • AUse a 'Until' activity to retry the stored procedure until it succeeds, and then use a 'Wait' activity to ensure the pipeline does not fail.
   • BChange the stored procedure to use a 'MERGE' statement with a 'TRY...CATCH' block that logs errors and continues.
   • CConfigure the stored procedure to use a transaction with a retry loop on deadlock errors, and set the pipeline to rerun on failure.
   • DSet the pipeline to rerun on failure and add a 'Lookup' activity before the stored procedure to check if the summary table is already updated.
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