DP-900 Describe an analytics workload on Azure Practice Question
A retail company ingests point-of-sale data into Azure Data Lake Storage Gen2, where it lands as CSV files. The analytics team wants to create a curated, query-optimized layer that can be consumed by Power BI and Azure Synapse Analytics. They plan to use Azure Databricks to transform the raw data. Which two capabilities does Azure Databricks provide for this analytics workload? (Choose two.)
⚠ Common exam trap
Many exam-takers confuse Azure Databricks with Azure Data Factory or Synapse dedicated SQL pools, which provide visual ETL or provisioned SQL warehouses rather than Spark-based notebooks.
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
✓
It provides an Apache Spark-based engine that can process large volumes of data in parallel.
Azure Databricks is a first-party Azure service built on Apache Spark, offering a collaborative notebook-based workspace and a distributed processing engine. For the retail company, these two capabilities directly support transforming raw CSV data into a curated, query-optimized layer. The other options describe features of different services or incorrect limitations, so they do not apply here.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
It provides an Apache Spark-based engine that can process large volumes of data in parallel.
Why this is correct
Azure Databricks is built on Apache Spark, which distributes processing across a cluster. In this scenario, the retail company needs to transform raw CSV files at scale, and Spark's in-memory, parallel execution handles large datasets efficiently. This makes it suitable for the transformation step before the curated layer is consumed by Power BI or Synapse.
- ✓
It includes a collaborative workspace with notebooks that support multiple languages such as Python, Scala, and SQL.
Why this is correct
Azure Databricks provides an interactive workspace where data engineers and analysts can collaborate using notebooks. These notebooks support Python, Scala, SQL, and R, allowing the team to write transformations in the language they prefer. This collaborative environment is a core part of the analytics workload, enabling shared development and versioning of data pipelines.
- ✗
It stores data in a proprietary format that cannot be read by other Azure services.
Why it's wrong here
Azure Databricks uses open formats such as Delta Lake, Parquet, and ORC, which are readable by many Azure services including Synapse Analytics and Power BI. A proprietary format would defeat the purpose of a shared data lake. Therefore, this statement is false and would not help the company build a curated layer accessible to other tools.
- ✗
It provides a fully managed extract, transform, and load (ETL) service with a drag-and-drop visual interface.
Why it's wrong here
Azure Databricks is a code-first, notebook-oriented analytics platform, not a drag-and-drop ETL tool. The visual, code-free pipeline authoring experience is provided by Azure Data Factory or Synapse pipelines. While Databricks can be orchestrated by those services, it does not itself offer a fully managed visual ETL interface for this workload.
- ✗
It automatically creates a dedicated SQL pool for each notebook session.
Why it's wrong here
Azure Databricks does not create dedicated SQL pools. Dedicated SQL pools are a feature of Azure Synapse Analytics, used for provisioned, MPP-based data warehousing. In this scenario, the company is using Databricks for transformation, so expecting automatic SQL pool creation is incorrect and would lead to unnecessary cost and architectural confusion.
Go deeper
Related to this question
Learn chapter
Synapse Dedicated vs Serverless SQL Pools
Key term
Data Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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