Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You manage a Microsoft Fabric lakehouse that must ingest a real-time feed of 150 000 JSON events per second from Azure Event Hubs. The solution has the following requirements:
Land the events in Delta tables in the lakehouse bronze folder with sub-second latency.
Guarantee exactly-once delivery even if the ingestion job is retried or restarted. Which ingestion approach should you implement to meet the requirements?
Enable continuous data import in a KQL database and expose the data to the lakehouse by creating a shortcut.
Build a data pipeline with a Copy activity that continuously copies data from Event Hubs to the lakehouse.
Create an Eventstream in Real-Time Intelligence and configure it to write directly to the lakehouse.
Develop a Spark Structured Streaming notebook that reads from Azure Event Hubs and writes to Delta tables in the lakehouse by using a checkpoint location.
Real-Time Intelligence Eventstream can read from Azure Event Hubs and write directly to lakehouse Delta tables with sub-second latency. When writing to Delta Lake, Eventstream uses transactional commits that provide an exactly-once guarantee across job restarts. Spark Structured Streaming can achieve exactly-once semantics but typically incurs higher latency at this throughput, Copy activities are batch not streaming, and KQL continuous import writes into a Kusto database rather than directly into Delta tables. Therefore, Eventstream is the only option that satisfies both the latency and exactly-once requirements.
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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