DP-900 Describe an analytics workload on Azure Practice Question
Which TWO tools can be used to transform data in an Azure data pipeline?
⚠ Common exam trap
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.
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
✓
Azure Databricks notebooks
Azure Databricks notebooks (C) are correct because they run Apache Spark code (Python, Scala, SQL, R) that can perform arbitrary data transformations such as filtering, joining, aggregating, and cleansing at scale within an Azure data pipeline. Azure Data Factory Data Flow (E) is correct because it provides a visual, code-free way to build scalable data transformation logic (mapping data flows) that executes on Azure Databricks-backed Spark clusters as part of an ADF pipeline. Power BI Desktop (A) is a reporting and visualization tool, not a pipeline data transformation service. Microsoft Purview (B) is a data governance, cataloging, and lineage service rather than a transformation engine. Azure Storage Explorer (D) is a client utility for browsing and managing storage accounts, not for transforming data.
Answer analysis
Option-by-option breakdown
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
Power BI Desktop is a self-service business intelligence tool for data visualization, report authoring, and interactive modeling. Although it includes Power Query for data shaping and cleaning, that transformation happens locally within the report's data model, not as part of a reusable Azure pipeline. It is not designed to scale or run as an automated ETL engine, so it does not transform data in an Azure data factory context.
- ✗
Microsoft Purview
Why it's wrong here
Microsoft Purview is an enterprise data governance and cataloging service that helps discover, classify, and track lineage across on-premises and cloud data sources. It does not execute any transformation logic; its role is to manage metadata and provide compliance visibility. While Purview can show how data was transformed, it never processes or rewrites the data itself, so it cannot be used to perform data transformations.
- ✓
Azure Databricks notebooks
Why this is correct
Azure Databricks notebooks are a correct transformation tool because they provide a collaborative, code-first environment running on Apache Spark for distributed data processing. Engineers can execute Python, Scala, SQL, or R to perform complex cleanses, joins, aggregations, and machine learning pre-processing, then write results to any connected data store. The notebooks can be triggered by Azure Data Factory pipelines, enabling automated, production-grade transformations at scale.
- ✗
Azure Storage Explorer
Why it's wrong here
Azure Storage Explorer is a client-side GUI tool used to upload, download, and manage files and blobs in Azure Storage accounts. It supports copy/move operations and even file sharing, but it contains no data processing engine to reshape or compute on data. It is simply a management utility for storage, so it cannot transform data as part of an Azure pipeline.
- ✓
Azure Data Factory Data Flow
Why this is correct
Azure Data Factory Data Flow is a correct transformation tool that allows users to design visual ETL pipelines without writing code. These data flows are executed on serverless Apache Spark clusters under the hood, enabling large-scale transformations such as joins, pivoting, and custom expressions through a drag-and-drop interface. Because it is integrated with ADF scheduling and monitoring, it is a fully managed, scalable transformation option for Azure data engineers.
Go deeper
Related to this question
Learn chapter
Databricks Delta Live Tables
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
Key term
Pipeline
A pipeline is an automated series of steps that takes code from development to production, ensuring quality and speed.
About these practice questions
Courseiva writes every DP-900 question from scratch — 851 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.