Microsoft Fabric Data Engineer Associate DP-700 Practice Question
Your company uses Microsoft Fabric Real-Time Intelligence. Azure Event Hub receives JSON telemetry from thousands of IoT devices. You must build a solution that discards records where sensorStatus equals "offline", converts temperature from Fahrenheit to Celsius, and writes the cleansed stream to both a Lakehouse Delta table and a KQL database-all with minimal code and low latency. Which approach should you take?
Develop a Fabric notebook that uses Spark Structured Streaming to read from Event Hub, perform the transformations in PySpark, and write separately to Delta Lake and a Kusto connector.
Create an Eventstream with the Event Hub as input, add a streaming query that filters out offline sensors and converts the temperature, and configure two outputs: one Lakehouse Delta table and one KQL database, then start the stream.
Deploy an Azure Stream Analytics job to process the Event Hub data and output to Azure Data Explorer and ADLS Gen2, then create shortcuts to the files in the Lakehouse for reporting.
Schedule a data pipeline that runs every minute, copies Event Hub capture files into the Lakehouse, and triggers an ingestion command that loads the data into a KQL database.
Eventstream is the low-code streaming component in Microsoft Fabric. It can ingest data directly from Azure Event Hub, apply inline SQL-like transformations to filter out unwanted records and calculate new columns, and route the processed stream simultaneously to multiple built-in sinks such as Lakehouse (Delta) and KQL database. Spark notebooks, Azure Stream Analytics, or batch pipelines would require more custom code, extra configuration, or introduce additional latency.
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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