A data pipeline with a storage event trigger delivers the highest-throughput file ingestion

DP-700 By islever Editorial Last updated Sep 26, 2026

Ingest and transform batch data

Short answer

A A data pipeline runs from storage event triggers when a new file arrives and copies the 500 GB files at the highest throughput unchanged.

Question

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?

Data pipeline Correct Answer
Environment
KQL queryset
Dataflow Gen2

Key Takeaways

Pipelines pair storage event triggers with the highest-throughput copy engine, which is exactly what large daily files need; environments and KQL querysets are not ingestion items at all.

For 500 GB files added daily to an external source, a data pipeline both triggers on file-arrival events and copies the data into Lakehouse1 at the highest throughput without applying any transformations.

Common mistake: Overlooking the pipeline because its storage trigger was once in preview; the trigger is generally available now, and no other item combines event triggering with high-throughput copy.

Expert Analysis

Why the Answer Is Correct

A data pipeline supports storage event triggers, so ingestion runs automatically each time a new file is added to the external source. The Copy data activity is the highest-throughput engine for moving large files as-is, which satisfies both the trigger and throughput requirements for the 500 GB daily files.

Why the Other Options Are Wrong

Option B (an Environment) configures Spark libraries and compute properties for notebooks; it does not ingest data. Option C (a KQL queryset) queries KQL databases and cannot copy files. Option D (Dataflow Gen2) applies Power Query transformations with lower throughput, the opposite of the no-transformation, high-throughput requirement.

Community Comment Notes

The community is unanimous (A 100). 2e6975f (3 likes) states a data pipeline is the most appropriate and efficient item for high-throughput, event-triggered ingestion of large files. 8d6881f notes the storage trigger was in preview as of January 2025 but concedes it is the best option, and the trigger is now generally available.

Official Reference

https://learn.microsoft.com/en-us/fabric/data-factory/pipeline-storage-event-triggers

This link opens the official vendor documentation in a new tab.

Community Votes

100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 3 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 3

2e6975f 👍 3 Answered A

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.
8d6881f 👍 1 Answered A

Data Pipeline is not right as at 2025-Jan as the storage trigger is still in preview, so it doesn't satisfy the requirement. But it's probably the best option.
henryphchan 👍 1 Answered A

same as the previous question

Reader comments & corrections

No reader corrections yet — spotted an error or have a note? Share it below.

← Back to DP-700 Study Guide