DP-700 Ingest and Transform Data Practice Question
You are building a data pipeline in Microsoft Fabric using a Copy Activity to ingest millions of small JSON files from an Azure Data Lake Storage Gen2 container into a Lakehouse table. The performance is significantly slower than expected due to high request latencies. Which optimization technique should you implement?
⚠ Common exam trap
Candidates often suggest increasing the cluster size, which is an expensive and ineffective solution for the 'small file problem' caused by high request latency during ingestion.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable staging in the Copy Activity settings to temporarily buffer data before writing it to the destination.
Enabling staging allows the Copy Activity to stage data in an intermediary storage account before writing to the Lakehouse, which efficiently compresses and batches requests. This drastically reduces the number of individual PUT operations against the storage endpoint, directly mitigating the throttling and latency issues commonly associated with processing massive volumes of small files in distributed analytics environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the Data Integration Units (DIUs) to the maximum allowable limit of 256 for the pipeline activity.
Why it's wrong here
Scaling up DIUs increases the compute power allocated to the copy operation, but when ingesting millions of small files, the bottleneck is predominantly network round-trip overhead and file metadata operations rather than raw compute throughput, rendering higher DIUs largely ineffective.
- ✓
Enable staging in the Copy Activity settings to temporarily buffer data before writing it to the destination.
Why this is correct
Staging uses an intermediary storage location to buffer and batch data payloads before executing bulk writes to the destination Lakehouse. This approach minimizes metadata overhead, reduces request latency, and optimizes network throughput when dealing with millions of small individual source files.
- ✗
Convert all source JSON files into single-line newline-delimited JSON format prior to executing the copy pipeline.
Why it's wrong here
While newline-delimited JSON improves parsing efficiency for downstream spark jobs, preprocessing millions of files outside the pipeline introduces unnecessary operational overhead and does not natively solve the underlying storage request latency bottleneck during ingestion.
- ✗
Switch the destination from a Delta Lake table in the Lakehouse to a raw staged folder within the Files section.
Why it's wrong here
Writing to a raw files folder avoids Delta transaction log overhead, but it fails to address the underlying protocol request latencies caused by handling numerous small files and leaves the data in an unstructured format requiring additional downstream transformation.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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