An eventstream filters the event hub feed to Chelsea and lands it in a lakehouse
Short answer
B An eventstream ingests the event hub feed, filters Neighbourhood == Chelsea, and stores the retained events in a lakehouse destination.
Question
Key Takeaways
Eventstream natively supports event hub sources, filter transformations, and a lakehouse destination, covering the entire filter-and-store flow without custom code.
An eventstream can ingest from the Azure event hub, apply a Filter transformation that keeps only events with Neighbourhood equal to Chelsea, and write the retained events to a Fabric lakehouse destination.
Common mistake: Choosing Spark Structured Streaming because eventstreams are assumed to target only KQL databases; eventstream also supports a lakehouse destination, and Spark would need custom code for the same flow.
Expert Analysis
Why the Answer Is Correct
An eventstream connects directly to an Azure event hub as a source, offers a Filter operator to retain only events where Neighbourhood equals Chelsea, and supports a Fabric lakehouse as a destination. This covers ingest, filter, and store in one no-code item, matching the requirement exactly.
Why the Other Options Are Wrong
Option A (a KQL queryset) only queries existing data; it does not ingest anything. Option C (a streaming dataset) is a Power BI in-memory construct for real-time visuals, not lakehouse storage. Option D (Apache Spark Structured Streaming) could implement the flow in code, but it takes more development effort than the built-in eventstream and is not required.
Community Comment Notes
The vote favors B (79 vs 21). 4371883 (3 likes) notes eventstream supports transformation activities such as filter and links the event hub source documentation, and MultiCloudIronMan (2 likes) lists ingest, filter, and lakehouse storage as native eventstream capabilities. amli123's dissent assumes eventstream only targets KQL databases, but the lakehouse destination is supported.
Official Reference
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Community Votes
79% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Discussion 4 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.
From public community threads 4
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GHill1982 👍 6 Answered BEventstream would be most suitable for this.
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amli123 👍 3 Answered DThe questions says to store the data in a Fabric lakehouse -> correct answer should be a apache spark structured streaming. Using an eventstream stored the data in KQL database, with optional availability in OneLake, but the questions doesnt state anything about KQL databases, so structured streaming should be the correct answer.
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4371883 👍 3 Answered BEventstream is the way to go here. It allows for transformation activities such as filter. https://learn.microsoft.com/en-us/fabric/real-time-intelligence/event-streams/add-source-azure-event-hubs?pivots=enhanced-capabilities
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MultiCloudIronMan 👍 2 Answered BAn eventstream is designed to handle this type of real-time data processing. It provides the necessary capabilities to: Ingest data from sources like Azure Event Hubs. Filter and transform the data in real-time. Store the processed data directly into a destination like a Fabric lakehouse.
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