Manage Batches and Pipelines DP-203 Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A data engineering team uses Azure Synapse Analytics and wants to implement version control for their pipelines. They have a requirement that all changes must be reviewed by a senior engineer before being published to the live service. They use GitHub for source control. What should they configure?

   • AExport all pipeline definitions as JSON files and store them in a separate GitHub repository.
   • BCreate a second Synapse workspace for development and manually copy changes to production.
   • CUse the built-in version history in Synapse Studio to track changes.
   • DEnable Git integration in Synapse Studio and use a GitHub repository with a branch policy requiring pull requests.
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2. 7application · medium

   A financial services company has an Azure Data Factory pipeline that loads daily transaction files from a partner's SFTP server into an Azure SQL database. The pipeline runs successfully, but the business team has noticed that some files contain more rows than expected, indicating possible duplicate or extra records. The team wants to add validation to the pipeline to catch such issues before the data is used for reporting. What should they add to the pipeline?

   • AAdd a 'Get Metadata' activity to check the file size before the copy.
   • BSet the Copy activity's 'Validate data consistency' option to enable row count validation.
   • CAdd a 'Lookup' activity after the copy to count rows in the destination and compare with the source file's row count.
   • DUse a 'Data Flow' activity to deduplicate the data before loading.
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4. 8foundation · easy

   Which Azure Synapse Pipeline trigger type is best suited for processing data in fixed time intervals while ensuring that each interval is processed exactly once?

   • AEvent-based trigger
   • BManual trigger
   • CTumbling window trigger
   • DSchedule trigger
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5. 9foundation · easy

   Which Azure Synapse component provides the compute resources for running Spark jobs?

   • ASpark pool
   • BSQL pool
   • CPipeline
   • DData flow
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6. 10expert · hard

   A company uses Azure Data Factory to load data from a legacy system that only exports files at unpredictable times. The files are dropped into an Azure Blob Storage container. The pipeline must process each file exactly once as soon as it arrives, but the team also needs to ensure that if the pipeline fails, the file is not lost and can be reprocessed. They have a requirement to minimize cost by not running a scheduler 24/7. What should they do?

   • AUse a tumbling window trigger with a 15-minute interval and check for new files in the pipeline.
   • BUse an event-based trigger on blob creation and configure the pipeline to move the file to a 'processed' folder after successful processing.
   • CUse a schedule trigger that runs every hour and checks for new files.
   • DUse a manual trigger and have an operator run the pipeline when files are uploaded.
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