DP-900 Describe core data concepts Practice Question
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?
⚠ Common exam trap
Test-takers frequently 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.
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
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.
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 Databricks
Why this is correct
Azure Databricks provides a unified analytics platform with Apache Spark, offering collaborative notebooks, full Git integration, and auto-scaling clusters. It supports both Python and SQL natively, making it ideal for interactive data exploration and large-scale transformation of data stored in Azure Data Lake Storage Gen2. Its managed infrastructure and notebook environment allow data engineers to prepare and process data efficiently, which aligns perfectly with the requirement.
- ✗
Azure Machine Learning studio
Why it's wrong here
Azure Machine Learning studio is designed specifically for the machine learning lifecycle, including experiment tracking, model training, and deployment. While it can perform some data preparation, that functionality is typically embedded within ML pipelines and is not intended for general-purpose, ad-hoc data lake processing. It lacks the comprehensive data engineering and interactive notebook capabilities needed for broad data preparation tasks across ADLS Gen2.
- ✗
Azure Data Studio
Why it's wrong here
Azure Data Studio is a lightweight desktop tool geared primarily toward SQL Server and Azure SQL database administration and querying. It does not natively connect to Azure Data Lake Storage Gen2 for distributed data processing, nor does it support Apache Spark or Python-based data transformation at scale. For data preparation in a data lake, it is out of scope because it targets relational database management, not big data analytics.
- ✗
Azure Synapse Studio
Why it's wrong here
Azure Synapse Studio does offer notebooks and some Git integration, but it is primarily built around SQL analytics and data warehousing workloads. Its Git integration is more limited and less seamless than Databricks, especially for collaborative code review and version control of notebook code. While it can process data in ADLS Gen2, its strengths are in serverless SQL and pipeline orchestration rather than interactive data preparation, making Databricks a better fit.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
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
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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