DP-900 Describe core data concepts Practice Question
Your organization uses Microsoft Fabric to build a data lakehouse. Data engineers need to transform data using Spark and store results in Delta Lake format. Which Fabric component should they use?
⚠ Common exam trap
Test-takers frequently confuse Pipelines (which orchestrate activities) with the actual compute engine (Notebooks) that runs Spark transformations, leading them to select Pipelines as the component for executing Spark code.
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
✓
Notebooks
Notebooks in Microsoft Fabric provide an interactive environment for writing and executing Spark code, which is required for transforming data using Spark. The results can be directly written to Delta Lake format, making Notebooks the correct component for this task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataflows Gen2
Why it's wrong here
Dataflows Gen2 in Microsoft Fabric rely on Power Query Online to provide a low-code, visual data preparation experience, but they do not run on the Spark engine. While they can perform common shaping and cleansing tasks, they are intended for citizen data preparers rather than code-first engineering workloads. Because the requirement involves using Spark and Delta Lake for a data lake, Dataflows Gen2 are the wrong tool as they lack native Delta Lake write operations and distributed processing capabilities.
- ✗
Pipelines
Why it's wrong here
Pipelines are Microsoft Fabric's orchestration and scheduling service, modeled after Azure Data Factory, and they coordinate activities such as copying data or invoking Spark notebooks, but they never execute data transformations themselves. A pipeline can start a notebook job that transforms data, yet the transformation logic resides in the notebook, not in the pipeline. Thus, while pipelines are useful for automating a flow, they are not a direct transformation mechanism and therefore do not meet the stated requirement.
- ✓
Notebooks
Why this is correct
Notebooks in Microsoft Fabric are the recommended code-based interface for working with Apache Spark, and they natively support popular languages like Python, Scala, and SQL. They can read raw data from files, apply transformations, and write results to Delta Lake tables within a lakehouse, leveraging ACID transactions and efficient upserts. Because the scenario explicitly calls for Spark and building a data lake, notebooks are the correct tool for executing event-driven or ad-hoc transformation logic directly on the compute engine.
- ✗
Semantic models
Why it's wrong here
Semantic models provide the analytical abstraction layer for Power BI-style reporting, defining relationships, measures, and KPIs over already-prepared data in a lakehouse or warehouse. They do not contain data transformation logic nor do they generate or write files to a data lake. Consequently, using a semantic model as a transformation tool for 'building a data lake' reflects a fundamental misunderstanding of the separation of concerns between data engineering and data consumption.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Data lakehouse
A data lakehouse is a modern data architecture that combines the flexibility of a data lake with the reliability and performance of a data warehouse on a single platform.
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