Quick Answer
The answer is Azure Data Factory and Azure Databricks. These two services work together to perform data transformation in an Azure analytics pipeline because Azure Data Factory orchestrates and executes ETL workflows, including code-free mapping data flows, while Azure Databricks provides a high-performance Apache Spark environment for custom, code-intensive transformations. On the Microsoft Azure Data Fundamentals DP-900 exam, this pairing tests your understanding of how cloud-based ETL services and compute engines collaborate within a modern data pipeline. A common trap is to select only one service or to confuse Azure Synapse Analytics, which is primarily for data warehousing and analytics, not transformation. Remember the memory tip: “Factory orchestrates, Databricks computes” — Data Factory handles the pipeline orchestration and visual transformations, while Databricks handles the heavy lifting with custom code and advanced analytics.
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 Azure services can be used to perform data transformation in an analytics pipeline? (Choose two.)
Azure Data Lake Storage Gen2
Why wrong: Storage service, not for transformation.
Azure Event Hubs
Why wrong: Ingestion service, not transformation.
Azure Data Factory
Supports data flows for transformation.
Power BI
Why wrong: Visualization tool, transformations are limited.
Azure Databricks
Can run Spark jobs for complex 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 Data Factory
Azure Data Factory is a cloud-based ETL service that allows you to create data pipelines to transform data at scale using mapping data flows or by invoking external compute services like Azure Databricks. It supports code-free visual transformations as well as custom code via Azure HDInsight or Databricks, making it a core service for data transformation in analytics pipelines.
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 Lake Storage Gen2
Why it's wrong here
Storage service, not for transformation.
Azure Event Hubs
Why it's wrong here
Ingestion service, not transformation.
Azure Data Factory
Why this is correct
Supports data flows for transformation.
Related concept
Read the scenario before looking for a memorised answer.
Power BI
Why it's wrong here
Visualization tool, transformations are limited.
Azure Databricks
Why this is correct
Can run Spark jobs for complex transformations.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse storage services (Data Lake Storage) or ingestion services (Event Hubs) with transformation services, or assume that visualization tools like Power BI can perform data transformation, when in fact they only consume pre-transformed data.
Detailed technical explanation
Azure Data Factory uses integration runtimes to execute data flows, which are translated into Spark jobs running on Azure Databricks or Azure HDInsight under the hood, enabling distributed transformation of petabytes of data. Azure Databricks provides an Apache Spark-based analytics platform where transformations are written in Python, Scala, or SQL, and it can be orchestrated by Data Factory for end-to-end pipeline automation. A real-world scenario is a retail company using Data Factory to copy raw sales data from Azure Blob Storage to Data Lake Storage, then invoking a Databricks notebook to clean and aggregate the data before loading it into Azure Synapse for reporting.
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Describe an analytics workload on Azure — study guide chapter
Learn the concepts, then practise the questions
Describe an analytics workload on Azure practice questions
Targeted practice on this topic area only
All DP-900 questions
982 questions across all exam domains
Microsoft Azure Data Fundamentals DP-900 study guide
Full concept coverage aligned to exam objectives
DP-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise DP-900 questions linked to Describe core data concepts.
Practise DP-900 questions linked to Describe an analytics workload on Azure.
Practise DP-900 questions linked to Identify considerations for relational data on Azure.
Practise DP-900 questions linked to Describe considerations for working with non-relational data on Azure.
Practise DP-900 questions linked to DP-900 fundamentals.
Practise DP-900 questions linked to DP-900 scenario.
Practise DP-900 questions linked to DP-900 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 Data Factory — Azure Data Factory is a cloud-based ETL service that allows you to create data pipelines to transform data at scale using mapping data flows or by invoking external compute services like Azure Databricks. It supports code-free visual transformations as well as custom code via Azure HDInsight or Databricks, making it a core service for data transformation in analytics pipelines.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.