Quick Answer
The answer is Azure Data Factory and Azure Databricks, as both services are explicitly designed for data transformation within Azure data pipelines. Azure Data Factory provides a code-free visual interface through Mapping Data Flows, enabling transformations like aggregations, joins, and filtering at scale without writing code, while Azure Databricks offers a collaborative Apache Spark-based environment for complex, code-driven transformations using notebooks and clusters. On the DP-203 exam, this question tests your understanding of which services handle the "T" in ETL/ELT, often appearing as a straightforward multiple-select item where candidates mistakenly choose Azure Synapse Analytics or Azure Stream Analytics—both of which are primarily for analytics or real-time processing, not general-purpose transformation. A common trap is confusing orchestration with transformation: remember that Data Factory orchestrates and transforms, while Databricks transforms and computes. Memory tip: think "ADF for drag-and-drop, Databricks for code-heavy Spark jobs"—both transform, but in different ways.
This DP-203 practice question tests your understanding of develop data processing. 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 Azure services can be used to perform data transformation in a data pipeline? (Select two.)
Azure Data Factory
Data Factory offers mapping data flows and compute activities for transformation.
Azure Monitor
Why wrong: Monitor is for observability, not data transformation.
Azure Storage
Why wrong: Storage is for storing data, not transforming it.
Azure Event Hubs
Why wrong: Event Hubs is for data ingestion, not transformation.
Azure Databricks
Databricks provides a collaborative environment for data processing and transformation.
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 Data Factory
Azure Data Factory is a cloud-based ETL service that provides a code-free visual interface for orchestrating data movement and transformation at scale. It supports data flows, which allow you to perform transformations like aggregations, joins, and filtering without writing code, making it a correct choice for data transformation 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.
Azure Data Factory
Why this is correct
Data Factory offers mapping data flows and compute activities for transformation.
Related concept
Read the scenario before looking for a memorised answer.
Azure Monitor
Why it's wrong here
Monitor is for observability, not data transformation.
Azure Storage
Why it's wrong here
Storage is for storing data, not transforming it.
Azure Event Hubs
Why it's wrong here
Event Hubs is for data ingestion, not transformation.
Azure Databricks
Why this is correct
Databricks provides a collaborative environment for data processing and transformation.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse data ingestion services (like Event Hubs) or storage services (like Azure Storage) with transformation services, forgetting that transformation requires compute engines like Data Factory or Databricks.
Detailed technical explanation
Azure Data Factory's Mapping Data Flows execute transformations on Azure Databricks clusters under the hood, enabling scalable, code-free ETL. Azure Databricks, on the other hand, uses Apache Spark's DataFrame API and SQL to perform complex transformations like window functions, pivoting, and machine learning feature engineering. In a real-world scenario, you might use Data Factory to orchestrate a pipeline that triggers a Databricks notebook for heavy transformations, combining both services for optimal performance.
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
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Data Factory — Azure Data Factory is a cloud-based ETL service that provides a code-free visual interface for orchestrating data movement and transformation at scale. It supports data flows, which allow you to perform transformations like aggregations, joins, and filtering without writing code, making it a correct choice for data transformation 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-203 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-203 exam.
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