A data pipeline with a storage event trigger gives event-driven, high-throughput file ingestion
Ingest and transform batch data
AnswerCorrect answer: D - A data pipeline supports storage event triggers that run when a new file arrives and provides the highest throughput for copying large files unchanged.
You have a Fabric workspace that contains a lakehouse named Lakehouse1. In an external data source, you have data files that are 500 GB each. A new file is added every day. You need to ingest the data into Lakehouse1 without applying any transformations. The solution must meet the following requirements Trigger the process when a new file is added. Provide the highest throughput. Which type of item should you use to ingest the data?
100% of anonymous learners picked answer D.
Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
Pipelines support storage event triggers that run when a new file arrives, and they are the highest-throughput engine for copying large batch files; Eventstream and streaming datasets target real-time event streams, not 500 GB files.
For 500 GB files arriving daily in an external source, a data pipeline is the item that both triggers on new-file events and delivers the highest throughput when copying the data into Lakehouse1 without applying any transformations.
Picking Eventstream because the requirement says 'trigger when a new file is added'; Eventstream processes streaming records rather than large batch files, and Dataflow Gen2 adds transformation overhead with lower throughput.
Community Discussion (5 comments)
IshtarSQL👍 8Selected: D
Eventstream is designed for ingesting real-time or streaming data from sources like IoT devices or logs. It’s not optimized for batch processing or large files.
fassil👍 3Selected: D
D. Data pipeline. Data pipelines are designed to handle large volumes of data efficiently and can be configured to trigger the ingestion process automatically when new files are added to the external data source. They also provide high throughput, making them suitable for handling 500 GB files daily without applying any transformations.
henryphchan👍 1Selected: D
I would prefer using data pipeline although it is a preview feature. Eventstream and streaming data are designed for realtime events
2e6975f👍 2Selected: D
For high-throughput, event-triggered ingestion of large files into a lakehouse without transformations, Data pipeline is the most appropriate and efficient item in Fabric.
4371883👍 2Selected: C
Streaming dataset is the only answer that ticks the requirements for storage trigger and high throughput. Data Pipeline is not right as at 2025-Jan. The storage trigger is still in preview, so it doesn't satisfy the requirement. But it's probably the best option.
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Expert Analysis
Why the Answer Is Correct
A data pipeline in Fabric Data Factory supports storage event triggers, so the ingestion can run automatically when a new file is added to the external source. Pipelines are also the highest-throughput item for moving large volumes of data: a daily 500 GB file is a batch copy, which is exactly what the Copy data activity is optimized for, and it lands the file as-is with no transformation.
Why the Other Options Are Wrong
Option A (Eventstream) ingests real-time streaming records from sources such as IoT devices and logs; it is not designed for 500 GB batch files. Option B (Dataflow Gen2) applies Power Query transformations and is not the highest-throughput choice for raw file copies. Option C (Streaming dataset) is a Power BI construct for small real-time payloads, not large-file lakehouse ingestion.
Community Comment Notes
The community is near-unanimous (D 88). IshtarSQL (8 likes) notes Eventstream is designed for real-time streaming rather than large files, and fassil adds that pipelines handle large volumes efficiently and can be triggered automatically when new files appear. One dissenting comment argues the storage trigger was still in preview, but the pipeline storage event trigger documentation is now generally available.