Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You are designing a real-time telemetry solution in Microsoft Fabric. Business analysts must be able to query incoming sensor readings together with the last three months of historical data by using the same syntax that they currently use in Azure Data Explorer. The solution must offer built-in windowing, time-series functions, and automatic data retention without requiring you to write code. Which streaming engine should you choose?
Create a KQL database in Real-Time Analytics and ingest the stream directly into a KQL table.
Use Eventstream only to capture the data to a lakehouse and query it later with SQL.
Develop a Spark Structured Streaming notebook that writes the data to Delta tables in a lakehouse.
Deploy an Azure Stream Analytics job that writes to a Fabric Data Warehouse.
A KQL database in Real-Time Analytics uses the same Kusto Query Language that analysts already know from Azure Data Explorer. It can ingest high-throughput event streams, stores both hot and historical data in the same engine, provides built-in time-series and windowing functions, and lets you configure retention policies without writing code.
Spark Structured Streaming would require notebooks and Python/Scala code and lacks KQL's built-in analytical functions. Eventstream on its own is a data ingestion service; to query data you would still need to land it elsewhere and use another engine. Azure Stream Analytics jobs can output to Fabric but run outside the service and are managed separately, so they do not meet the requirement for a native Fabric engine with KQL querying.
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What is Kusto Query Language (KQL)?
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What is Real-Time Analytics in Microsoft Fabric?
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How does windowing work in time-series data analysis?
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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