Microsoft Fabric Data Engineer Associate DP-700 Practice Question
Your solution must ingest about one million IoT sensor events per second into Microsoft Fabric, auto-scale the compute, refresh operator dashboards in seconds, and let analysts run ad-hoc anomaly-detection queries by writing a familiar declarative language (not notebook code) against both real-time and historical data. Which Fabric streaming engine best meets these requirements?
T-SQL streaming ingestion into a lakehouse SQL endpoint
Spark Structured Streaming in a Fabric notebook
KQL streaming in Real-Time Analytics (Azure Data Explorer in Fabric)
KQL streaming in Microsoft Fabric's Real-Time Analytics is built on Azure Data Explorer technology. It can ingest well above one million events per second, automatically scales compute, stores the data in OneLake, and lets users run both real-time and historical queries with the same Kusto Query Language (KQL) without writing notebook code. Spark Structured Streaming would require writing code in notebooks, Eventstream alone does not provide a unified query surface for historical analysis, and T-SQL streaming into a lakehouse SQL endpoint cannot currently sustain multi-million-events-per-second ingestion.
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What is KQL and how is it used in Real-Time Analytics?
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Why is Spark Structured Streaming not ideal for this scenario?
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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