Practice Questions

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Question 1

What is the primary purpose of Azure Databricks?

A. Hosting relational databases
B. Managing file shares
C. Processing large-scale data using Apache Spark
D. Running virtual machines

✅ Answer: C

Explanation:
Azure Databricks is built on Apache Spark for large-scale data processing.

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Question 2

Which feature is a key characteristic of Azure Databricks?

A. Fixed schema relational tables
B. Distributed data processing
C. File-based storage only
D. Limited scalability

✅ Answer: B

Explanation:
Databricks uses distributed computing to process large datasets efficiently.

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Question 3

Which scenario is BEST suited for Azure Databricks?

A. Hosting a transactional database
B. Running large-scale ETL pipelines and machine learning models
C. Managing shared file storage
D. Serving static web pages

✅ Answer: B

Explanation:
Databricks is ideal for data engineering and machine learning at scale.

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Question 4

What is Microsoft Fabric primarily designed for?

A. Running operating systems
B. Providing a unified, end-to-end analytics platform
C. Managing virtual networks
D. Hosting relational databases only

✅ Answer: B

Explanation:
Microsoft Fabric integrates multiple analytics capabilities into one unified platform.

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Question 5

Which component of Microsoft Fabric serves as a unified data storage layer?

A. Azure Blob Storage
B. SQL Database
C. OneLake
D. Azure Files

✅ Answer: C

Explanation:
OneLake is the centralized storage layer within Microsoft Fabric.

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Question 6

Which service is BEST suited for organizations that want a single platform for data engineering, data warehousing, and BI?

A. Azure Virtual Machines
B. Azure Databricks
C. Microsoft Fabric
D. Azure Table Storage

✅ Answer: C

Explanation:
Fabric provides an end-to-end unified analytics experience.

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Question 7

Which of the following best describes the difference between Azure Databricks and Microsoft Fabric?

A. Databricks is for storage, Fabric is for compute
B. Databricks focuses on big data processing, Fabric provides a unified analytics platform
C. Fabric only supports relational data, Databricks does not
D. Databricks cannot scale, Fabric can

✅ Answer: B

Explanation:
Databricks focuses on processing and ML, while Fabric provides end-to-end analytics.

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Question 8

Which programming environments are commonly supported in Azure Databricks notebooks?

A. HTML and CSS only
B. Python, SQL, Scala, and R
C. JavaScript only
D. PowerShell only

✅ Answer: B

Explanation:
Databricks notebooks support multiple languages including Python, SQL, Scala, and R.

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Question 9

Which scenario is NOT ideal for Azure Databricks?

A. Large-scale data transformation
B. Machine learning model training
C. Managing simple file shares
D. Processing streaming data

✅ Answer: C

Explanation:
Databricks is not designed for file-sharing scenarios.

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Question 10

Which statement about Microsoft Fabric is TRUE?

A. It requires manual infrastructure management
B. It is a SaaS-based unified analytics platform
C. It only supports batch processing
D. It replaces all Azure services

✅ Answer: B

Explanation:
Microsoft Fabric is a fully managed SaaS platform that integrates analytics services.

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✅ Quick Exam Takeaways

✔ Azure Databricks

• Apache Spark-based
• Distributed processing
• Data engineering & machine learning

✔ Microsoft Fabric

• Unified analytics platform
• End-to-end solution (data + analytics + BI)
• Includes OneLake storage

✔ Key differences:

• Databricks → processing & ML
• Fabric → all-in-one analytics platform

✔ Exam tip:
👉 Big data processing → Azure Databricks
👉 Unified analytics platform → Microsoft Fabric

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Go to the DP-900 Exam Prep Hub main page.

This post is a part of the DP-900: Microsoft Azure Data Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Describe an analytics workload (25–30%)
   --> Describe common elements of large-scale analytics
      --> Describe Microsoft Cloud Services for large-scale analytics (Azure Databricks & Microsoft Fabric)

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

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Modern analytics workloads often require processing massive volumes of data quickly and efficiently. Microsoft provides powerful cloud services to meet these needs, including Azure Databricks and Microsoft Fabric.

For the DP-900 exam, you should understand what these services are, their key features, and when to use each.

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Why Large-Scale Analytics Services Matter

Large-scale analytics involves:

• Processing big data (TBs to PBs)
• Supporting batch and real-time workloads
• Enabling advanced analytics and machine learning

✔ Traditional tools often cannot scale to meet these demands.

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Azure Databricks

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What Is Azure Databricks?

Azure Databricks is a cloud-based analytics platform built on Apache Spark.

It is designed for:

• Big data processing
• Data engineering
• Machine learning
• Collaborative analytics

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Key Features

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1. Apache Spark-Based Processing

• Distributed computing engine
• Processes large datasets in parallel

✔ Ideal for big data workloads

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2. Collaborative Workspace

• Notebooks (Python, SQL, Scala, R)
• Multiple users can collaborate

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3. Integration with Azure

• Works with Azure Data Lake Storage
• Integrates with Azure Synapse Analytics

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4. Machine Learning Support

• Built-in ML capabilities
• Supports advanced analytics workflows

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Common Use Cases

• Big data processing (ETL/ELT pipelines)
• Data science and machine learning
• Real-time analytics
• Data transformation at scale

✔ Best for: Data engineers and data scientists working with large datasets

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Microsoft Fabric

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What Is Microsoft Fabric?

Microsoft Fabric is an end-to-end, unified analytics platform that brings together multiple data services into a single environment.

It integrates:

• Data engineering
• Data warehousing
• Data science
• Real-time analytics
• Business intelligence

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Key Features

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1. Unified Platform

• Combines multiple services into one
• Reduces complexity of managing separate tools

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2. OneLake (Unified Storage Layer)

• Centralized data lake for all workloads
• Eliminates data silos

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3. Integrated Analytics Experiences

• Data Factory (ingestion)
• Data Warehouse
• Real-Time Analytics
• Power BI integration

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4. SaaS-Based Model

• Fully managed platform
• Minimal infrastructure management

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Common Use Cases

• End-to-end analytics solutions
• Unified data platform for organizations
• Business intelligence and reporting
• Data integration and transformation

✔ Best for: Organizations wanting a single, unified analytics solution

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Azure Databricks vs Microsoft Fabric

Feature	Azure Databricks	Microsoft Fabric
Focus	Big data processing & ML	End-to-end analytics platform
Engine	Apache Spark	Multiple integrated engines
Users	Data engineers, data scientists	Broad (engineers, analysts, business users)
Complexity	More flexible, more technical	Simpler, unified experience
Use Case	Advanced analytics & ML	Unified analytics and BI

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How They Fit in an Analytics Architecture

Typical roles:

• Azure Databricks
  • Data processing
  • Advanced transformations
  • Machine learning
• Microsoft Fabric
  • End-to-end pipeline
  • Storage (OneLake)
  • Reporting (Power BI integration)

✔ They can complement each other in modern architectures.

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Key Considerations When Choosing

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Choose Azure Databricks when:

• You need advanced data engineering or machine learning
• You require Spark-based processing
• You want full control and flexibility

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Choose Microsoft Fabric when:

• You want a unified analytics platform
• You prefer simplified, integrated workflows
• You need end-to-end analytics in one place

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Why This Matters for DP-900

On the exam, you may be asked to:

• Identify the purpose of Azure Databricks
• Recognize Microsoft Fabric as a unified analytics platform
• Choose the right service for a scenario
• Understand how these services support large-scale analytics

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Summary — Exam-Relevant Takeaways

✔ Azure Databricks

• Apache Spark-based
• Big data processing
• Machine learning
• Flexible and powerful

✔ Microsoft Fabric

• Unified analytics platform
• End-to-end solution
• Includes data engineering, warehousing, and BI

✔ Key difference:

• Databricks → advanced processing & ML
• Fabric → all-in-one analytics platform

✔ Exam tip:
👉 Spark + big data processing → Azure Databricks
👉 Unified analytics platform → Microsoft Fabric

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Go to the Practice Exam Questions for this topic.

Go to the DP-900 Exam Prep Hub main page.