DP-700 Ingest and Transform Data Practice Question
You are using Dataflow Gen2 to ingest data from multiple sources. You need to perform a complex transformation that involves merging data from a SQL database and a flat file based on a common key. Which step should you take to achieve this efficiently?
⚠ Common exam trap
Candidates might choose complex SQL scripts or Spark notebooks for basic relational joins that can be handled natively and efficiently inside Dataflow Gen2.
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
✓
Use the Merge transformation
In Dataflow Gen2, the 'Merge' transformation allows users to join tables from different sources within the Power Query interface. By selecting the common key, you can combine datasets into a single model for downstream processing. This approach is highly efficient as it leverages the underlying mashup engine to perform the heavy lifting, ensuring the transformation is processed in a unified, maintainable data pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write a custom Python script
Why it's wrong here
While possible in notebooks, using custom Python scripts for simple merges is inefficient and harder to maintain compared to the native Dataflow GUI. Dataflow Gen2 is designed to abstract these complex operations into a low-code environment, providing better performance and native tracking of lineage within the Fabric workspace.
- ✓
Use the Merge transformation
Why this is correct
The Merge transformation is the native tool in Power Query for performing joins. It is specifically optimized to combine data from disparate sources based on a common key, providing a visual and intuitive way to manage relationships and ensure data consistency without requiring manual coding or external script management.
- ✗
Create a temporary SQL view
Why it's wrong here
Creating views in the source SQL database adds unnecessary dependency on the source system's compute resources. Dataflow Gen2 is designed to perform these transformations in the cloud, offloading the work from the source system to prevent performance degradation on production databases, which is a key principle of modern data engineering.
- ✗
Export both to CSV first
Why it's wrong here
Exporting data to intermediate CSV files introduces unnecessary latency and complexity. It breaks the automated ingestion flow and creates manual steps that are prone to error. Dataflow Gen2 can connect directly to both sources, allowing for seamless integration without the need for interim storage or manual file manipulation.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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