Manage Batches and Pipelines DP-203 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A logistics company has a nightly batch pipeline that loads shipment tracking data from a third-party API into Azure SQL Database. The pipeline uses a copy activity to fetch data from the API and a stored procedure to upsert records. Occasionally, the API returns an error, causing the pipeline to fail. The company wants to ensure that the pipeline automatically retries the failed run and that no duplicate records are inserted when the pipeline is rerun. What should you do?

   • AConfigure the copy activity with a retry policy and set the stored procedure to use a MERGE statement that checks for existing records.
   • BUse a 'Until' activity to retry the copy activity until it succeeds, and then run the stored procedure.
   • CAdd a 'Lookup' activity before the copy activity to check if the data already exists, and if so, skip the copy.
   • DUse a tumbling window trigger with rerun on failure and set the copy activity to use 'Fault Tolerance' with 'Skip incompatible rows'.
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2. 2application · medium

   A data engineering team is developing a new pipeline in Azure Data Factory. They want to use Git for version control and follow a branching strategy where the 'main' branch is for production, 'dev' branch for development, and feature branches for individual tasks. They also want to ensure that changes to the 'main' branch are only made through pull requests with at least one approval. What should they configure?

   • AUse Azure Data Factory's built-in version control and manually create branches in the UI.
   • BExport the pipeline JSON files and store them in a separate Git repository, then use Azure DevOps to manage branches.
   • CEnable Git integration and set the default branch to 'main', then use Azure DevOps branch policies to require pull request reviews.
   • DUse Azure Synapse Analytics workspace Git integration and configure branch policies in the Synapse workspace.
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4. 3application · medium

   A logistics company uses Azure Data Factory to run a pipeline that processes shipment tracking data. The pipeline must run every 6 hours, starting at 00:00 UTC. The team wants to ensure that the pipeline runs exactly at 00:00, 06:00, 12:00, and 18:00 UTC, even if the pipeline takes longer than 6 hours to complete. What type of trigger should they use?

   • ATumbling window trigger with a 6-hour window and 'Max concurrency' set to 1.
   • BManual trigger with a custom timer in the pipeline.
   • CSchedule trigger with a recurrence of every 6 hours.
   • DEvent-based trigger on a blob created every 6 hours.
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5. 4foundation · easy

   After a batch load completes, you need to verify that the row count in the destination table matches the source. Which Azure Data Factory activity is designed to perform row-count validation?

   • ALookup activity
   • BData Flow activity
   • CStored procedure activity
   • DIf Condition activity
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6. 5expert · hard

   A company runs a daily pipeline in Azure Data Factory that loads data from a CSV file in Blob Storage to a Synapse SQL pool. The pipeline has been running successfully, but the business team has reported that the data in the SQL pool is sometimes incomplete, even though the pipeline status shows 'Succeeded'. The team suspects that the source file may have more rows than the pipeline actually loads. They want to add validation to ensure that the number of rows loaded matches the source file. The source file is large (several GB) and the team wants to minimize the performance impact of validation. What should they do?

   • AUse the Copy activity's 'Validate data consistency' option to compare row counts.
   • BUse a pipeline activity to read the source file's row count from the file's metadata (e.g., by reading the file with a Lookup activity that uses a query to count rows) and compare it with the SQL pool count.
   • CUse a Data Flow activity to read the source file and count rows, then compare with the SQL pool count.
   • DAfter the Copy activity, use a Lookup activity to count rows in the SQL pool and a Get Metadata activity to get the file's row count, then compare them.
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