DP-700 Ingest and Transform Data Practice Question
Which Fabric tool would you use to perform a visual, low-code data transformation that directly results in a clean table in your Lakehouse?
⚠ Common exam trap
Candidates frequently mix up Dataflow Gen2 with Data Pipelines or Notebooks, failing to realize that Dataflow Gen2 uses Power Query specifically for visual, low-code data transformations.
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
✓
Dataflow Gen2
Dataflow Gen2 provides a highly intuitive, low-code interface for complex transformations. It uses the Power Query engine to allow users to connect, clean, and transform data visually. Once the transformations are complete, the data can be loaded directly into a Fabric Lakehouse, making it the perfect tool for non-programmers to create clean, production-ready tables without needing to manage code-based pipelines or notebooks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Notebook
Why it's wrong here
Notebooks require programming skills (Python/Scala) and are best suited for advanced data engineering or science tasks. They are not the 'low-code' tool recommended for simple visual transformations. Using a notebook for tasks that can be done in Dataflow Gen2 is an unnecessary use of complex compute resources.
- ✗
Data Pipeline
Why it's wrong here
Pipelines are for orchestration and movement, not for performing deep data transformations. While they can trigger transformations, the actual logic for cleaning and shaping data should reside in Dataflow or Notebooks. Using a pipeline for the transformation itself is a misuse of the tool's core functionality.
- ✓
Dataflow Gen2
Why this is correct
Dataflow Gen2 is explicitly designed for low-code data preparation. It offers a familiar Power Query experience to transform data and load it into a Lakehouse. It is the most effective tool for users who want to build clean data models without writing code, offering visual lineage and easy management.
- ✗
SQL Endpoint
Why it's wrong here
The SQL endpoint is for querying the Lakehouse, not for transforming the data before it lands. It is a consumption layer tool, not an ingestion or preparation tool. Relying on the SQL endpoint for transformations would require writing complex stored procedures or views after the data is already stored.
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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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