The answer is Azure Databricks and Azure Data Factory, as both services are designed to perform data transformation within a data pipeline. Azure Databricks provides an Apache Spark-based analytics platform that handles complex transformations, such as ETL operations, by allowing you to write code in Python, Scala, or SQL within notebooks and clusters, making it ideal for large-scale, code-driven data reshaping. Azure Data Factory, meanwhile, offers a code-free, visual interface for orchestrating and executing transformations through data flows, mapping, and compute activities. On the DP-900 exam, this question tests your understanding of which services handle the “transform” stage in a pipeline, often contrasting them with storage or ingestion services like Azure Blob Storage or Event Hubs. A common trap is to confuse Azure Synapse Analytics, which is more for data warehousing and analytics, with a dedicated transformation service. Memory tip: think of Databricks as the “code wrangler” for heavy lifting and Data Factory as the “visual conductor” for orchestrated flows.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. 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? (Choose two.)
Why wrong: Blob Storage is a storage service, not a transformation service.
B
Azure SQL Database
Why wrong: SQL Database is a relational database; transformation can be done via stored procedures but not as a primary pipeline transformation service.
C
Azure Databricks
Databricks provides Spark-based transformation.
D
Azure Event Hubs
Why wrong: Event Hubs is for data ingestion, not transformation.
E
Azure Data Factory
Data Factory provides data flows for transformation.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Databricks
Azure Databricks is correct because it provides an Apache Spark-based analytics platform that can perform complex data transformations, such as ETL (Extract, Transform, Load) operations, using notebooks and clusters. It allows you to write code in Python, Scala, or SQL to transform data at scale, making it a core compute service 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
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Azure Blob Storage
Why it's wrong here
Blob Storage is a storage service, not a transformation service.
✗
Azure SQL Database
Why it's wrong here
SQL Database is a relational database; transformation can be done via stored procedures but not as a primary pipeline transformation service.
✓
Azure Databricks
Why this is correct
Databricks provides Spark-based transformation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Event Hubs
Why it's wrong here
Event Hubs is for data ingestion, not transformation.
✓
Azure Data Factory
Why this is correct
Data Factory provides data flows for transformation.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse storage or ingestion services (like Blob Storage or Event Hubs) with compute services that actually execute transformation logic, leading them to select options that only move or store data.
Detailed technical explanation
How to think about this question
Azure Data Factory uses a visual pipeline designer or JSON-based definitions to orchestrate data movement and transformation, leveraging mapping data flows that run on Spark clusters under the hood. Azure Databricks provides a collaborative environment where you can write custom transformation logic using Apache Spark's DataFrame API, which is optimized for in-memory distributed processing. In a real-world scenario, you might use Data Factory to copy raw data from Blob Storage into Databricks, then apply complex joins and aggregations before loading the results into Azure Synapse Analytics.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Databricks — Azure Databricks is correct because it provides an Apache Spark-based analytics platform that can perform complex data transformations, such as ETL (Extract, Transform, Load) operations, using notebooks and clusters. It allows you to write code in Python, Scala, or SQL to transform data at scale, making it a core compute service for data transformation in a pipeline.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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