DP-700 Ingest and Transform Data Practice Question
You are building a Medallion architecture in Fabric. You have ingested raw CSV files into the 'Files' section of your Bronze Lakehouse. You now need to transform this data and load it into a Delta table in the Silver Lakehouse. Which method provides the best performance and scalability for this transformation?
⚠ Common exam trap
Candidates often choose 'Dataflow Gen2' for all transformations. While Dataflows work, Spark Notebooks are specifically preferred for complex, large-scale transformations in a Medallion architecture due to superior processing power.
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 Notebooks using Spark
In a Medallion architecture, the transition from Bronze to Silver typically involves cleaning, filtering, and converting data into the Delta format. Spark Notebooks are the preferred tool for this stage because they offer massive parallel processing capabilities and deep integration with the Delta Lake API, which is essential for maintaining performance as data volumes grow.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Power BI Dataflows (Gen1)
Why it's wrong here
Power BI Dataflows Gen1 are legacy components primarily used for Power BI datasets and are not integrated into the Fabric Lakehouse architecture in the same way as newer components. They lack the native Delta Lake writing optimizations and performance characteristics required for a scalable Silver tier in a medallion architecture.
- ✗
Data Factory Pipelines with a Lookup activity
Why it's wrong here
Lookup activities are used for retrieving small amounts of metadata or configuration and are not designed for large-scale data transformation or loading. Using them for Bronze-to-Silver processing would be extremely inefficient and would not scale to handle even moderately sized datasets in a production environment.
- ✓
Fabric Notebooks using Spark
Why this is correct
Fabric Notebooks leverage the Spark engine to perform distributed data processing. They are ideal for transforming raw files into Silver-level Delta tables because they can handle schema enforcement, complex logic, and high-volume data efficiently. This approach ensures that the Silver layer is optimized for downstream analytical workloads.
- ✗
Direct T-SQL 'SELECT INTO' from the SQL Endpoint
Why it's wrong here
The SQL Endpoint of a Lakehouse is read-only for tables created via Spark. While you can use T-SQL in a Warehouse, you cannot use it to write directly into Lakehouse Delta tables from the SQL endpoint. This limitation makes it an invalid choice for the primary transformation logic in a Lakehouse-centric architecture.
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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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