DP-700 Ingest and Transform Data Practice Question
You need to perform a complex transformation that involves multiple joins across tables of varying sizes in Fabric. Which tool provides the best balance between performance and ease of use?
⚠ Common exam trap
Examinees tend to recommend writing complex PySpark scripts for every transformation, overlooking the low-code yet powerful visual capabilities of Dataflows 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
✓
Fabric Dataflow Gen2.
Dataflows Gen2 are specifically designed for low-code ETL, providing a visual interface that translates into efficient Spark jobs. They handle complex joins and transformations natively, making them ideal for standard data engineering tasks. They abstract the complexity of Spark configuration while providing enough power to manage large-scale data manipulation, making them the superior choice for most transformation scenarios compared to manual coding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Microsoft Purview Data Catalog.
Why it's wrong here
Purview is a governance and metadata management tool, not an execution engine for data transformation. It provides insights into data lineage and classification but lacks the computational capabilities required to perform joins or transformations on datasets. It cannot execute ETL logic or manipulate table data.
- ✓
Fabric Dataflow Gen2.
Why this is correct
Dataflow Gen2 offers a user-friendly Power Query interface that generates optimized Spark code for transformations. It allows for complex data cleansing, merging, and shaping without requiring deep coding expertise, making it perfect for developers who need to implement business logic across multiple disparate tables efficiently within Fabric.
- ✗
A SQL stored procedure on an on-premises server.
Why it's wrong here
Executing transformations on-premises limits scalability and creates high latency when working with cloud-based data. Fabric is designed to perform transformations within its own storage layer to maximize throughput and minimize egress costs. Moving data back and forth to an on-premises server is counter-productive and inefficient.
- ✗
Azure Data Factory pipeline parameters only.
Why it's wrong here
Parameters are used to control the configuration of activities, not for performing actual data transformations. While you can pass parameters into activities, they do not provide the logic engine needed to perform relational joins or data cleansing operations. They are strictly configuration elements, not data processing tools.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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