Quick Answer
The answer is Azure Data Factory Data Flow and Azure Databricks notebooks. These two tools are correct because Data Flow provides a visual, code-free interface for designing data transformations at scale within a pipeline, while Databricks notebooks offer a collaborative, code-based environment using Apache Spark for complex ETL operations in Python, Scala, SQL, or R. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of the core transformation options available in Azure Data Factory, often appearing as a multiple-select item where you must distinguish between data movement tools (like Copy Activity) and actual transformation tools. A common trap is confusing a simple data copy with a transformation step, so remember that transformation always involves changing the data’s structure or schema. Memory tip: think “Flow for visuals, Notebooks for code” to recall the two distinct paths for transforming data in a pipeline.
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO tools can be used to transform data in an Azure data pipeline?
Power BI Desktop
Why wrong: Used for data visualization and modeling, not pipeline transformation.
Microsoft Purview
Why wrong: Data catalog and governance, not transformation.
Azure Databricks notebooks
Enables code-based transformations using Spark.
Azure Storage Explorer
Why wrong: File management tool, not transformation.
Azure Data Factory Data Flow
Allows visual ETL transformations.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure Databricks notebooks
Azure Databricks notebooks are correct because they provide an interactive, collaborative environment for data transformation using Apache Spark. You can write code in Python, Scala, SQL, or R to perform complex ETL operations, and the notebooks integrate directly with Azure Data Factory as a compute target in a pipeline.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Power BI Desktop
Why it's wrong here
Used for data visualization and modeling, not pipeline transformation.
Microsoft Purview
Why it's wrong here
Data catalog and governance, not transformation.
Azure Databricks notebooks
Why this is correct
Enables code-based transformations using Spark.
Related concept
Read the scenario before looking for a memorised answer.
Azure Storage Explorer
Why it's wrong here
File management tool, not transformation.
Azure Data Factory Data Flow
Why this is correct
Allows visual ETL transformations.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse data transformation tools with data governance or storage management tools, leading them to select options like Microsoft Purview or Azure Storage Explorer, which serve entirely different purposes in the Azure analytics ecosystem.
Detailed technical explanation
Azure Data Factory Data Flow allows you to design visually based, code-free transformation logic that runs at scale on Azure Databricks or Azure HDInsight Spark clusters. Under the hood, Data Flows are translated into Spark jobs that execute on a serverless cluster, enabling operations like joins, aggregations, and pivots without writing code. In real-world scenarios, Data Flows are ideal for low-code ETL, while Databricks notebooks are preferred when custom logic or advanced machine learning transformations are needed.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Databricks notebooks — Azure Databricks notebooks are correct because they provide an interactive, collaborative environment for data transformation using Apache Spark. You can write code in Python, Scala, SQL, or R to perform complex ETL operations, and the notebooks integrate directly with Azure Data Factory as a compute target in a pipeline.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.
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