Ingest and Transform Streaming Data DP-700 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   In Spark Structured Streaming, what is the purpose of a 'sink'?

   • ATo transform the streaming data.
   • BTo define the source of the streaming data.
   • CTo manage the checkpointing of the stream.
   • DTo define where the streaming output is written.
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2. 17expert · hard

   A healthcare company needs to ingest HL7 messages from a hospital system. The messages must be transformed (e.g., field mapping, filtering) and then stored for both real-time monitoring and long-term batch analytics. The team has limited engineering resources and prefers minimal custom code. They also need to handle occasional spikes in message volume. Which approach best balances these requirements?

   • AUse Spark Structured Streaming to ingest, transform, and write to both a KQL database and a Lakehouse.
   • BUse a KQL database with update policies to transform the data and then export to a Lakehouse.
   • CUse an Eventstream to ingest from the hospital system, apply transformations, and send data to both a KQL database for real-time monitoring and a Lakehouse for batch analytics.
   • DUse Dataflow Gen2 to ingest and transform the data, then load to a Lakehouse and a KQL database.
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4. 18foundation · easy

   In Microsoft Fabric Real-Time Intelligence, what is the primary difference between a native table and a OneLake shortcut?

   • AA native table is only for batch data, while a shortcut is for streaming data.
   • BA native table is read-only, while a shortcut supports writes.
   • CA native table stores data in the KQL database, while a shortcut references data stored elsewhere in OneLake or external sources.
   • DA native table requires a gateway, while a shortcut does not.
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5. 19application · medium

   A media company ingests video analytics events from multiple sources. They need to perform complex transformations, including joining with reference data and aggregating over event-time windows, and then write the results to a delta table for batch reporting. The team is proficient in Python and Spark. What should you use?

   • AUse Spark Structured Streaming to read from the sources, perform the joins and windowed aggregations, and write to the delta table.
   • BUse KQL to query the streaming sources directly and apply windowing functions.
   • CUse an Eventstream to perform the joins and aggregations and route to the lakehouse.
   • DUse Azure Stream Analytics to perform the transformations and write to the lakehouse.
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6. 20foundation · easy

   Which KQL operator is used to filter rows in a streaming data stream?

   • Aproject
   • Bextend
   • Cwhere
   • Dsummarize
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