Quick Answer
Azure Databricks is the correct choice because it provides a collaborative notebook environment with native Python and SQL support, integrates seamlessly with Git for version control, and offers auto-scaling clusters that dynamically adjust compute resources based on workload demand on your Azure Data Lake Storage Gen2 data lake. This service is purpose-built for big data analytics and data preparation, using a managed Spark infrastructure that minimizes management overhead by handling resource provisioning automatically. On the DP-900 exam, this scenario tests your ability to distinguish between Azure Databricks and other data services like Azure Synapse Analytics or HDInsight—a common trap is confusing Databricks’ collaborative notebooks with Synapse’s SQL-focused pipelines. Remember that if the question emphasizes collaborative notebooks, Git integration, and auto-scaling on a data lake, Databricks is your answer. Memory tip: “Databricks = Data + Bricks (building blocks for notebooks and auto-scaling clusters).”
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.
Your company has a data lake in Azure Data Lake Storage Gen2 containing terabytes of parquet files. Data scientists need to explore and prepare this data using Python and SQL. They want to use a collaborative notebook environment that integrates with Git for version control. The solution should automatically scale compute resources based on workload demand and minimize management overhead. Which Azure service should you use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Azure Databricks
Provides notebooks, Git integration, auto-scaling, and supports Python and SQL.
Azure Machine Learning studio
Why wrong: Focused on ML experimentation, not general data preparation.
Azure Data Studio
Why wrong: Desktop tool for SQL Server, not for distributed data lake processing.
Azure Synapse Studio
Why wrong: Notebooks exist but Git integration is limited compared to Databricks.
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 Databricks
Azure Databricks is the correct choice because it provides a collaborative notebook environment that natively supports Python and SQL, integrates with Git for version control, and offers auto-scaling clusters that dynamically adjust compute resources based on workload demand. It is purpose-built for big data analytics and data preparation on data lakes, minimizing management overhead through its serverless and managed Spark infrastructure.
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 Databricks
Why this is correct
Provides notebooks, Git integration, auto-scaling, and supports Python and SQL.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Azure Machine Learning studio
Why it's wrong here
Focused on ML experimentation, not general data preparation.
Azure Data Studio
Why it's wrong here
Desktop tool for SQL Server, not for distributed data lake processing.
Azure Synapse Studio
Why it's wrong here
Notebooks exist but Git integration is limited compared to Databricks.
Common exam traps
The trap here is that candidates often confuse Azure Synapse Studio with Databricks because both offer notebook experiences and Spark support, but Synapse Studio is optimized for enterprise data warehousing and ETL pipelines, not the ad-hoc, collaborative data exploration and auto-scaling flexibility that Databricks provides for data science teams.
Detailed technical explanation
Azure Databricks uses Apache Spark clusters that can be configured with autoscaling, where the cluster dynamically adds or removes nodes based on the workload's CPU and memory demands, ensuring cost efficiency and performance. The Git integration is achieved through Databricks Repos, which allows users to sync notebooks with remote Git repositories (e.g., GitHub, Azure Repos) for version control and collaboration. In a real-world scenario, data scientists can mount the ADLS Gen2 data lake using service principals and directly read parquet files into Spark DataFrames, then use Python (e.g., PySpark) or SQL (via Spark SQL) to transform the data without manual infrastructure management.
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
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Databricks — Azure Databricks is the correct choice because it provides a collaborative notebook environment that natively supports Python and SQL, integrates with Git for version control, and offers auto-scaling clusters that dynamically adjust compute resources based on workload demand. It is purpose-built for big data analytics and data preparation on data lakes, minimizing management overhead through its serverless and managed Spark infrastructure.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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