Orchestrate Processes DP-700 Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A data platform team wants to trigger a Fabric pipeline whenever a new file lands in a specific Azure Data Lake Storage (ADLS) Gen2 container. The pipeline should process only the newly arrived file. What should the team configure?

   • AA notebook that runs continuously and watches the container for new files
   • BA scheduled trigger that runs every 5 minutes and checks for new files
   • CAn event-based trigger on the ADLS container that starts the pipeline when a blob is created
   • DA Dataflow Gen 2 that is set to refresh whenever new data is detected
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2. 7foundation · easy

   Which of the following is a common use case for scheduling a data pipeline?

   • ATriggering a pipeline when a file is uploaded to a storage account
   • BManually executing a pipeline for testing
   • CRunning a pipeline only when a previous pipeline fails
   • DRunning a daily data refresh at 6:00 AM
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4. 8application · medium

   A data engineering team needs to ingest data from a REST API into a Lakehouse table, then perform a series of transformations that include merging with a reference table and aggregating results. The team wants to minimize the number of separate artifacts to maintain and prefers a low-code approach for the transformation logic. What should you recommend?

   • ACreate a pipeline with a Copy activity to ingest the API data, followed by a Dataflow Gen 2 activity for transformations.
   • BCreate a Dataflow Gen 2 that ingests the API data and performs the merge and aggregation, then load the result to the Lakehouse table.
   • CCreate a notebook that uses PySpark to call the API, merge, and aggregate, then schedule the notebook directly.
   • DCreate a pipeline with a Notebook activity that contains all the logic, and schedule the pipeline.
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5. 9foundation · easy

   What is the primary purpose of Dataflow Gen 2 in Microsoft Fabric?

   • ATo store and manage large volumes of relational data
   • BTo create and run data transformation logic using a visual, low-code interface
   • CTo orchestrate the execution of multiple data activities in a sequence
   • DTo execute Python and Scala code for advanced analytics
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6. 10expert · medium

   A data engineering team must build a solution that ingests data from a REST API, performs complex transformations using a custom Python library, and loads the output to a lakehouse. The team has a strict requirement to minimize the number of artifacts and prefers to use a single tool. What should they use?

   • AA pipeline with a Copy activity and a Dataflow Gen 2
   • BA Fabric notebook that uses Python to call the API, transform data, and write to the lakehouse
   • CA pipeline with a Web activity and a Notebook activity
   • DA Dataflow Gen 2 with a Python script step
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