Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You are building an eventstream in Microsoft Fabric to ingest JSON telemetry from an Azure Event Hub. The solution must satisfy two requirements:
Analysts need to run ad-hoc KQL queries over the arriving data with sub-second latency.
A raw, append-only copy of every event must be stored in Delta/Parquet format for later batch processing. Which set of eventstream sinks should you configure to meet both requirements with the fewest components?
A KQL database sink writes incoming events to a Real-Time Analytics database, enabling immediate, low-latency querying by using Kusto Query Language. A Lakehouse sink stores the same events as Delta tables (Parquet files) in OneLake, providing a raw, replayable history for downstream batch or ML workloads. Using both sinks together meets the real-time query and durable storage goals without any extra services. The other options fail to deliver one of the two requirements: a SQL Data Warehouse sink does not support KQL; a Power BI streaming dataset cannot persist full-fidelity history; mirroring an Event Hub namespace still requires another target for storage and KQL access.
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Microsoft Fabric Data Engineer Associate DP-700
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