Ingest and Transform Streaming Data DP-700 Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   A data engineer needs to ingest a high-velocity stream of IoT sensor data into Microsoft Fabric and perform real-time transformations before landing the data in a KQL database. Which streaming engine is specifically designed for this scenario?

   • AEventstreams
   • BSQL Analytics endpoint
   • CDataflows (Gen2)
   • DSpark Structured Streaming
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   What is the primary benefit of enabling query acceleration on a OneLake shortcut in Real-Time Intelligence?

   • AIt allows the shortcut to write data back to the source.
   • BIt automatically converts the shortcut to a native table.
   • CIt reduces the need for data compression.
   • DIt creates a cached copy of the data to improve query performance.
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A ride-sharing company needs to compute the average trip duration per city for the last 30 minutes, updating every 5 minutes. They are using Spark Structured Streaming. Which windowing approach should they use?

   • AUse a sliding window with a 30-minute duration and 5-minute slide.
   • BUse a tumbling window with a 30-minute duration.
   • CUse a global window with a 30-minute watermark.
   • DUse a session window with a 30-minute gap.
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A telemetry company receives IoT device messages in JSON format through an Event Hubs instance. They need to aggregate temperature readings per device over 5-minute windows and write the results to a delta table in a lakehouse for downstream reporting. The team is comfortable writing code in notebooks. What should you do?

   • AUse an Eventstream to read from Event Hubs, aggregate using a built-in windowing transformation, and route to the lakehouse.
   • BUse Azure Stream Analytics to aggregate and write to the lakehouse.
   • CUse KQL to query the Event Hubs data directly and apply a windowing function.
   • DUse Spark Structured Streaming to read from Event Hubs, apply a 5-minute tumbling window aggregation, and write the output to the delta table.
   Reveal Solution Discussion Select an answer first
6. 10application · medium

   A financial services company needs to ingest real-time stock trade messages from an Azure Event Hubs instance. They want to validate the message schema, drop malformed messages, and route the valid messages to both a KQL database for real-time analytics and a lakehouse for long-term storage. The team prefers a no-code solution. What should you use?

   • AUse Event Hubs capture to store raw messages in a lakehouse and then use KQL to query the lakehouse directly.
   • BCreate two separate Eventstreams: one for KQL database and one for lakehouse, each with its own validation logic.
   • CCreate an Eventstream with Event Hubs as the source, add a transformation to validate and filter, and add two destinations: KQL database and lakehouse.
   • DUse Spark Structured Streaming to read from Event Hubs, validate messages in code, and write to both KQL database and lakehouse.
   Reveal Solution Discussion Select an answer first