Ingest and Transform Streaming Data DP-700 Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   What is the purpose of a windowing function in streaming data analysis?

   • ATo group events into time-based intervals for aggregation.
   • BTo filter out duplicate events.
   • CTo convert streaming data into batch data.
   • DTo sort events by timestamp.
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2. 22application · medium

   A retail company needs to ingest clickstream events from a Kafka topic into Fabric Real-Time Intelligence. They want to filter out bot traffic and enrich each event with a customer segment lookup before the data lands in a KQL database for near-real-time dashboards. The team has minimal infrastructure to manage and prefers a low-code approach. What should you recommend?

   • ACreate an Eventstream that reads from Kafka, uses a transformation to filter and enrich the data, and routes the output to a KQL database.
   • BUse Spark Structured Streaming to read from Kafka, apply the filter and enrichment in a notebook, and write to a KQL database.
   • CUse Eventhouse to ingest Kafka data directly, then apply KQL update policies for filtering and enrichment.
   • DCreate a OneLake shortcut to the Kafka topic and query it directly from a KQL database.
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4. 23application · medium

   A gaming company needs to process player telemetry streams to detect cheating patterns. They require complex event processing, such as correlating events across multiple streams and computing session-level aggregates. The data science team wants to use Python and deploy the logic as a reusable job. What should they use?

   • AA KQL database with update policies to process the streams.
   • BSpark Structured Streaming in a Fabric notebook, reading from Event Hubs, joining streams, and writing results to a Lakehouse.
   • CA Dataflow Gen2 to ingest and process the streaming data.
   • DAn Eventstream with multiple sources and derived streams.
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5. 24foundation · easy

   In KQL, which function is used to create a time-based window for aggregating streaming data?

   • Anow()
   • Bbin()
   • Cextend()
   • Dago()
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6. 25expert · hard

   A utility company needs to ingest smart meter readings from a Kafka cluster. The data must be filtered for abnormal readings, aggregated per neighborhood every 10 minutes, and stored in a KQL database for real-time alerts. They also need to retain raw data for compliance. The team wants to minimize operational overhead. What should they do?

   • AUse Spark Structured Streaming to ingest from Kafka, filter, aggregate, and write to both a KQL database and a Lakehouse.
   • BUse Dataflow Gen2 to ingest from Kafka and load to a Lakehouse only.
   • CUse a KQL database with a Kafka ingestion mapping and update policies.
   • DUse an Eventstream with a Kafka source, filter operation, tumbling window aggregation, and send results to a KQL database while also sending raw data to a Lakehouse.
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