DP-900 Microsoft Azure Data Fundamentals Syllabus Overview 2025

Introduction

This document provides an outline for the DP-900 Microsoft Azure Data Fundamentals syllabus, covering the key concepts and skills required for data professionals in the cloud.

The DP-900 exam assesses candidates' understanding of core Azure data services, including data storage, processing, and analytics. It also covers data governance and security, as well as Azure data tools and technologies.

This outline serves as a roadmap for preparing for the DP-900 exam, ensuring candidates have a comprehensive understanding of the essential concepts and are well-prepared for success.

A Brief Introduction To the DP-900 Microsoft Azure Data Fundamentals Exam.

The DP-900 Microsoft Azure Data Fundamentals exam assesses candidates' foundational knowledge of Azure data services and concepts. It covers core data concepts and technologies, including relational and non-relational databases, data storage, analytics, and data governance. The syllabus encompasses various Azure data services, such as Azure SQL, Cosmos DB, Azure Data Lake, and Azure Synapse Analytics.

Individuals preparing for the DP-900 exam should possess a basic understanding of cloud computing and data management principles. Familiarity with Azure core services and concepts, including Azure Resource Manager and Azure Active Directory, is also beneficial. By passing this exam, candidates demonstrate their proficiency in core data concepts and Azure data services, laying the groundwork for further specialization in Azure data technologies.

Importance of this Certification For Beginners in Cloud and Data Management.

The DP-900 Microsoft Azure Data Fundamentals certification holds significant importance for beginners in cloud and data management. It provides a foundational understanding of Azure data services and concepts, equipping individuals with the necessary knowledge to navigate the rapidly evolving field of cloud computing and data analytics.

Earning the DP-900 certification demonstrates a commitment to professional development and validates an individual's grasp of core data concepts and technologies. It serves as a stepping stone for aspiring data professionals, cloud engineers, and anyone seeking to enhance their skills in data management and analytics. By obtaining this certification, beginners can differentiate themselves in the job market and increase their employability.

Furthermore, the DP-900 syllabus aligns with industry best practices and covers essential Azure data services, providing a solid foundation for further specialization in Azure data technologies. It empowers individuals to make informed decisions regarding data storage, processing, and analytics, enabling them to contribute effectively to data-driven organizations.

What’s New in the 2025 Syllabus Update?

The 2025 syllabus update for the DP-900 Microsoft Azure Data Fundamentals exam introduces several significant changes and additions, reflecting the evolving landscape of cloud data technologies and industry best practices.

One key update is the inclusion of content on Azure Synapse Analytics, a powerful data analytics service that combines data integration, data warehousing, and big data analytics capabilities. Candidates will be tested on their understanding of Synapse's architecture, data ingestion methods, and analytics features.

Additionally, the updated syllabus places greater emphasis on data governance and security. Candidates will be expected to demonstrate proficiency in data classification, access control, and data protection measures within Azure data services. This reflects the growing importance of data privacy and compliance in today's data-centric world.

Furthermore, the syllabus now covers Azure Purview, a data governance service that enables organizations to manage and govern their data assets across multiple sources. Candidates will be tested on their understanding of Purview's capabilities for data discovery, lineage tracking, and data quality management.

Overall, the 2025 syllabus update for the DP-900 exam ensures that candidates possess the most up-to-date knowledge and skills required to succeed in the field of Azure data management and analytics.

1. Describe Core Data Concepts (25-30%)

The "Core Data Concepts" section of the DP-900 Microsoft Azure Data Fundamentals exam syllabus covers the foundational concepts and terminology related to data management and analytics in Azure. Candidates will be tested on their understanding of:

• Data types and data structures
• Data models, including relational and non-relational models
• Data storage concepts, such as tables, columns, and rows
• Data processing techniques, including data extraction, transformation, and loading (ETL)
• Data analysis concepts, such as data aggregation, filtering, and visualization

This section also assesses candidates' knowledge of Azure data services and their capabilities, including Azure SQL Database, Azure Cosmos DB, Azure Data Lake Storage, and Azure Synapse Analytics. Candidates should be familiar with the purpose and benefits of each service, as well as their key features and limitations.

A thorough understanding of core data concepts and Azure data services is essential for individuals seeking to build a strong foundation in Azure data management and analytics.

Types of Data (Structured, Semi-Structured, Unstructured)

The DP-900 Microsoft Azure Data Fundamentals exam syllabus includes a section on data types, covering structured, semi-structured, and unstructured data. Candidates will be tested on their understanding of the characteristics and use cases of each data type, as well as the Azure services and tools used to manage and analyze them.

Structured Data is highly organized and conforms to a predefined schema, making it easy to store and query in relational databases like Azure SQL Database. Examples of structured data include customer records, financial transactions, and product catalogs.

Semi-structured data has a less rigid structure than structured data, but it still contains some organizational elements, such as tags or key-value pairs. JSON and XML are common formats for semi-structured data. Azure Cosmos DB is a suitable service for managing and querying semi-structured data.

Unstructured Data lacks a predefined structure and can be difficult to process and analyze. Examples of unstructured data include images, videos, and text documents. Azure Data Lake Storage is a service designed for storing and processing large volumes of unstructured data.

Understanding the different types of data and their appropriate storage and analysis techniques is crucial for effective data management and analytics in Azure.

Relational vs. Non-Relational Data

The DP-900 Microsoft Azure Data Fundamentals exam syllabus includes a section on relational vs. non-relational data, covering the key characteristics and use cases of each data model. Candidates will be tested on their understanding of the advantages and disadvantages of both models, as well as the Azure services that support them.

Relational data is organized into tables, with rows and columns representing individual data points. Each row has a unique identifier, and relationships between data points are established through foreign keys. Azure SQL Database is a relational database service that provides high performance and scalability.

Non-relational data (also known as NoSQL) does not follow the traditional table structure. Instead, it uses flexible data structures such as key-value pairs, documents, or graphs. Azure Cosmos DB is a non-relational database service that offers high availability, low latency, and global distribution.

Relational databases are well-suited for structured data that requires complex queries and joins. **Non-relational databases** are more appropriate for unstructured or semi-structured data, and they excel at handling large volumes of data and scaling elastically.

Understanding the differences between relational and non-relational data is essential for choosing the right data storage and management solution for a given application or workload.

Data Processing (Batch vs. Streaming)

The DP-900 Microsoft Azure Data Fundamentals exam syllabus includes a section on data processing, covering both batch and streaming data processing techniques. Candidates will be tested on their understanding of the advantages and disadvantages of each approach, as well as the Azure services that support them.

Batch data processing involves processing large volumes of data that are collected and stored over some time. This data is typically processed offline, using tools like Azure Data Factory or Azure HDInsight. Batch processing is suitable for tasks such as data warehousing, data analytics, and reporting.

Streaming data processing involves processing data as it is generated, in real-time or near real-time. This data is typically processed online, using tools like Azure Stream Analytics or Azure Event Hubs. Streaming processing is suitable for tasks such as fraud detection, anomaly detection, and real-time analytics.

Batch processing is generally more cost-effective and reliable, but it can be slower than streaming processing. **Streaming processing** is more expensive and complex, but it can provide real-time insights and enable immediate action.

Understanding the differences between batch and streaming data processing is essential for choosing the right approach for a given application or workload.

Roles and Responsibilities In The Data Domain

The DP-900 Microsoft Azure Data Fundamentals exam syllabus includes a section on roles and responsibilities in the data domain, covering the various job roles involved in data management and analytics. Candidates will be tested on their understanding of the key responsibilities and skills required for each role.

Some common roles in the data domain include:

• Data Engineer: Designs, builds, and maintains data infrastructure, including data pipelines, data warehouses, and data lakes.
• Data analyst: Analyzes data to extract insights and identify trends. Develops and maintains data analysis models and reports.
• Data scientist: Uses advanced statistical and machine learning techniques to build predictive models and solve complex data problems.
• Database administrator: Manages and maintains databases, ensuring data integrity, security, and performance.
• Data Architect: Designs and implements data management solutions, including data models, data governance policies, and data security measures.

Understanding the different roles and responsibilities in the data domain is important for effective collaboration and communication within data teams.

2. Describe how to work with Relational Data on Azure (20-25%)

The "How to Work with Relational Data on Azure" section of the DP-900 Microsoft Azure Data Fundamentals exam syllabus covers the concepts and techniques for managing and querying relational data in Azure. Candidates will be tested on their understanding of:

• Azure SQL Database, including its features, capabilities, and use cases
• Creating and managing Azure SQL databases
• Writing and executing SQL queries
• Data types, data constraints, and data relationships
• Indexing and performance optimization
• Data security and access control

This section also assesses candidates' knowledge of Azure Synapse Analytics, a cloud-based data warehouse that combines the power of SQL with big data analytics capabilities. Candidates should be familiar with the benefits of using Azure Synapse Analytics for large-scale data warehousing and analytics workloads.

A solid understanding of relational data management and Azure SQL Database is essential for individuals seeking to build data-driven applications and solutions on Azure.

Azure relational Data Services (Azure SQL Database, Azure Database for MySQL, PostgreSQL, etc.)

The DP-900 Microsoft Azure Data Fundamentals exam syllabus includes a section on Azure relational data services, covering the various managed database offerings that Azure provides. Candidates will be tested on their understanding of the key features, benefits, and use cases of each service.

Some of the key Azure relational data services include:

• Azure SQL Database: A fully managed relational database service that supports both SQL Server and PostgreSQL engines.
• Azure Database for MySQL: A fully managed relational database service for MySQL.
• Azure Database for PostgreSQL: A fully managed relational database service for PostgreSQL.
• Azure Cosmos DB: A globally distributed, multi-model database service that supports both relational and non-relational data.

Candidates should be familiar with the different deployment options, pricing models, and performance characteristics of each service. They should also understand how to choose the right service for a given application or workload.

A thorough understanding of Azure relational data services is essential for individuals seeking to build data-driven applications and solutions on Azure.

Concepts of database normalization and relationships

The DP-900 Microsoft Azure Data Fundamentals exam syllabus includes a section on database normalization and relationships, covering the fundamental concepts and techniques for designing and managing efficient and effective relational databases.

Database normalization is the process of organizing data in a database to reduce data redundancy and improve data integrity. Normalization involves dividing data into multiple tables based on their relationships and dependencies. The goal of normalization is to create a database structure that is free from data anomalies, such as data duplication, insertion anomalies, and deletion anomalies.

Database relationships define the connections between different tables in a database. Relationships are used to enforce data integrity and ensure that data is consistent across tables. The most common types of database relationships are one-to-one, one-to-many, and many-to-many.

Candidates will be tested on their understanding of the different normalization forms (1NF, 2NF, 3NF, BCNF), as well as the different types of database relationships. They should also be able to apply normalization techniques to real-world data scenarios.

A solid understanding of database normalization and relationships is essential for individuals seeking to design and manage relational databases effectively.

Basic SQL Querying and Data Management In Relational Databases

The DP-900 Microsoft Azure Data Fundamentals exam syllabus includes a section on basic SQL querying and data management in relational databases, covering the fundamental concepts and techniques for querying and managing data in Azure SQL Database.

Candidates will be tested on their understanding of the following SQL concepts:

• Data Definition Language (DDL): Used to create, modify, and delete database objects such as tables, columns, and indexes.
• Data Manipulation Language (DML): Used to insert, update, and delete data in tables.
• Data Query Language (DQL): Used to retrieve data from tables.

Candidates should be able to write basic SQL queries to perform tasks such as:

• Selecting data from tables
• Filtering data based on specific criteria
• Sorting data in ascending or descending order
• Grouping data and performing aggregate functions
• Joining data from multiple tables

Candidates should also be familiar with basic data management tasks such as creating and managing tables, inserting and updating data, and deleting data.

A solid understanding of basic SQL querying and data management is essential for individuals seeking to work with relational databases in Azure.

3. Describe how to work with Non-Relational Data on Azure (15-20%)

The "How to Work with Non-Relational Data on Azure" section of the DP-900 Microsoft Azure Data Fundamentals exam syllabus covers the concepts and techniques for managing and querying non-relational data in Azure. Candidates will be tested on their understanding of:

• Non-relational data models, including document, key-value, and graph models
• Azure Cosmos DB, including its features, capabilities, and use cases
• Creating and managing Azure Cosmos DB databases and containers
• Writing and executing queries using the Azure Cosmos DB SQL API
• Data partitioning and indexing in Azure Cosmos DB
• Data security and access control in Azure Cosmos DB

This section also assesses candidates' knowledge of other Azure services for non-relational data, such as Azure Table Storage and Azure Blob Storage. Candidates should be familiar with the different capabilities and use cases of each service.

A solid understanding of non-relational data management and Azure Cosmos DB is essential for individuals seeking to build scalable and flexible data-driven applications on Azure.

Non-Relational Data Stores (NoSQL, Key-Value, Document, Column-Family, Graph Databases)

The DP-900 Microsoft Azure Data Fundamentals exam syllabus includes a section on non-relational data stores, also known as NoSQL databases. Candidates will be tested on their understanding of the different types of NoSQL data models and the Azure services that support them.

Some of the key types of NoSQL data models include:

• Key-value stores: Store data as a collection of key-value pairs.
• Document stores: Store data as JSON documents.
• Column-family stores: Store data in tables with rows and columns, but where each column is stored separately.
• Graph databases: Store data as a graph of nodes and edges.

Azure provides a range of NoSQL data services, including:

• Azure Cosmos DB: A globally distributed, multi-model database service that supports all of the major NoSQL data models.
• Azure Table Storage: A key-value store that is designed for storing large amounts of structured data.
• Azure Blob Storage: An object store that is designed for storing unstructured data, such as images and videos.

Candidates should be familiar with the different capabilities and use cases of each service. A solid understanding of non-relational data stores is essential for individuals seeking to build scalable and flexible data-driven applications on Azure.

Azure non-relational data services (Cosmos DB, Blob Storage, Table Storage)

Azure offers a comprehensive suite of non-relational data services that cater to diverse data storage and processing needs. Cosmos DB is a fully managed, globally distributed database service that supports multiple data models (document, key -value, graph, and table) and delivers high availability, scalability, and low latency.

Blob Storage provides a cost-effective and durable solution for storing unstructured data such as images, videos, and documents. It offers massive scalability, hierarchical organization, and secure access control.

Table Storage, on the other hand, is a NoSQL service that excels in storing structured data in the form of key-value pairs, making it ideal for scenarios requiring efficient and scalable data retrieval.

These services are essential components of the Azure data ecosystem, enabling developers to build modern data-driven applications that handle large volumes of data with flexibility, performance, and cost-effectiveness.

4. Describe an Analytics Workload on Azure (25-30%)

Azure provides a comprehensive set of services to support diverse analytics workloads. These services enable organizations to extract valuable insights from their data, regardless of its size, format, or complexity.

One common analytics workload on Azure involves using Azure Data Lake Storage to store and process large volumes of raw data. This data can be structured, semi-structured, or unstructured, and can come from various sources such as IoT devices, social media, and transaction systems.

To analyze the data, organizations can leverage Azure's powerful compute and analytics services such as Azure HDInsight and Azure Synapse Analytics. These services provide scalable and cost-effective platforms for running complex analytics algorithms and generating insights. Additionally, Azure offers a range of machine learning and artificial intelligence services that can enhance the accuracy and efficiency of data analysis.

The results of the analysis can be visualized and shared using Azure's data visualization and reporting tools, such as Power BI and Azure Data Studio. These tools empower business users and analysts to explore the data, identify trends, and make informed decisions.

By leveraging Azure's analytics services, organizations can gain deeper insights into their data, improve operational efficiency, and drive innovation.

Analytics Services in Azure (Azure Synapse Analytics, Azure Data Lake, Databricks)

Azure offers a comprehensive suite of analytics services that empower organizations to extract

valuable insights from their data. These services include:

Azure Synapse Analytics is a fully managed analytics service that combines data integration, data warehousing, and big data analytics capabilities. It provides a unified platform for ingesting, storing, and analyzing data from diverse sources, enabling organizations to gain a holistic view of their data.

Azure Data Lake is a scalable and secure data lake service that allows organizations to store and process vast amounts of structured, semi-structured, and unstructured data. It provides a cost-effective and flexible platform for storing raw data, enabling organizations to perform big data analytics and machine learning.

Databricks is a cloud-based data analytics platform that provides a unified environment for data engineering, data science, and machine learning. It offers a wide range of tools and libraries that enable data professionals to collaborate and perform complex analytics tasks.

Together, these services provide a powerful ecosystem for organizations to unlock the value of their data and drive data-driven decision-making. By leveraging Azure's analytics services, organizations can gain deeper insights into their data, improve operational efficiency, and accelerate innovation.

Use Cases For Modern Data Warehouses And Data Lakes

Modern data warehouses and data lakes offer a range of use cases that empower organizations to unlock the value of their data and drive data-driven decision-making. Some common use cases include:

Data analytics and reporting: Data warehouses and data lakes provide a centralized platform for storing and analyzing large volumes of data from diverse sources. This enables organizations to gain a holistic view of their data and perform complex analytics to identify trends, patterns, and insights.

Machine learning and artificial intelligence: Data warehouses and data lakes provide a foundation for building machine learning and AI models. These models can be used to automate tasks, improve decision-making, and create personalized experiences.

Data science and research: Data warehouses and data lakes provide a scalable and secure environment for data scientists and researchers to explore and analyze data. They can use these platforms to develop new algorithms, test hypotheses, and collaborate on data-driven projects.

Customer relationship management (CRM): Data warehouses and data lakes can be used to store and analyze customer data from multiple touchpoints. This enables organizations to gain a better understanding of their customers' behavior, preferences, and needs.

Fraud detection and risk management: Data warehouses and data lakes can be used to detect and prevent fraud by analyzing large volumes of transaction data. They can also be used to assess risk and make informed decisions.

By leveraging modern data warehouses and data lakes, organizations can gain valuable insights from their data, improve operational efficiency, and drive innovation.

Azure Data Factory for ETL and Data Movement

Azure Data Factory is a cloud-based data integration service that enables organizations to create and manage data pipelines for extracting, transforming, and loading (ETL) data. It provides a scalable and reliable platform for moving data between on-premises systems, cloud services, and data warehouses.

Azure Data Factory offers a range of features and capabilities that simplify the process of data integration, including:

• Pre-built connectors: Azure Data Factory provides a library of pre-built connectors that enable seamless integration with various data sources and sinks, such as relational databases, cloud storage services, and SaaS applications.
• Data transformation: Azure Data Factory provides a powerful data transformation engine that allows users to perform a wide range of data transformations, such as filtering, sorting, joining, and aggregating data.
• Scheduling and orchestration: Azure Data Factory enables users to schedule and orchestrate data pipelines, ensuring that data is processed and moved at the right time and in the right order.
• Monitoring and management: Azure Data Factory provides comprehensive monitoring and management capabilities that allow users to track the progress of data pipelines, identify errors, and troubleshoot issues.

By leveraging Azure Data Factory, organizations can streamline their data integration processes, improve data quality, and gain valuable insights from their data. It is a key component of the Azure data ecosystem, enabling organizations to build modern data-driven applications and solutions.

Real-Time Analytics With Azure Stream Analytics and Power BI

Real-time analytics involves analyzing data as it is being generated, enabling organizations to respond to events and make informed decisions in near real-time. Azure Stream Analytics and Power BI are two powerful tools that can be used together to perform real-time analytics on data streams.

Azure Stream Analytics is a fully managed event processing engine that enables organizations to analyze high volumes of streaming data in real-time. It provides a simple and scalable platform for filtering, aggregating, and transforming data streams, and generating real-time insights.

Power BI is a business intelligence and data visualization tool that allows users to create interactive dashboards and reports. By integrating Azure Stream Analytics with Power BI, organizations can visualize and analyze real-time data, gaining insights that can help them make informed decisions and respond to changing conditions.

Together, Azure Stream Analytics and Power BI provide a comprehensive solution for real-time analytics. Organizations can use these tools to:

• Monitor and analyze real-time data from IoT devices, sensors, and other sources.
• Detect anomalies and patterns in real-time, enabling organizations to respond quickly to events.
• Create real-time dashboards and reports that provide up-to-date insights into business operations.

By leveraging Azure Stream Analytics and Power BI, organizations can gain a competitive advantage by making data-driven decisions in real-time.

Preparation Resources (Dumpsarena)

Preparing for the DP-900 Microsoft Azure Data Fundamentals exam is essential for individuals looking to demonstrate their foundational knowledge of Azure data services. Dumpsarena offers a range of preparation resources to help candidates succeed in the exam.

Dumpsarena's practice tests are designed to simulate the actual exam experience. These tests cover all the objectives outlined in the DP-900 syllabus, providing candidates with an in-depth understanding of the key concepts and skills required for the exam.

In addition to practice tests, Dumpsarena also provides study notes and video tutorials that cover the exam topics in detail. These resources are developed by experienced Azure professionals and provide clear and concise explanations of the concepts.

Dumpsarena's preparation resources are designed to help candidates:

• Identify their strengths and weaknesses
• Focus their studies on the most important topics
• Gain confidence in their ability to pass the exam

By utilizing Dumpsarena's preparation resources, candidates can increase their chances of success in the DP-900 Microsoft Azure Data Fundamentals exam and demonstrate their proficiency in Azure data services.

DP-900 Microsoft Azure Data Fundamentals Exam Preparation Questions

Core Data Concepts

1. Which of the following best describes structured data?

a) Data stored in a flexible schema 

b) Data stored in a fixed schema, like rows and columns 

c) Data stored in JSON format 

d) Data stored in a graph database 

2. What is the primary characteristic of semi-structured data?

a) It has no schema 

b) It has a fixed schema 

c) It has a self-describing structure (e.g., JSON, XML) 

d) It is stored in relational databases 

3. Which of the following is an example of unstructured data?

a) CSV files 

b) JSON files 

c) Email messages 

d) Relational database tables 

4. What is the primary purpose of a data warehouse?

a) To store real-time transactional data 

b) To store and analyze large volumes of historical data 

c) To store unstructured data 

d) To store data in a graph format 

5. Which of the following is a key characteristic of a data lake?

a) It only stores structured data 

b) It stores raw data in its native format 

c) It requires a fixed schema 

d) It is optimized for transactional workloads 

Relational Data in Azure

6. Which Azure service is a fully managed relational database?

a) Azure Cosmos DB 

b) Azure SQL Database 

c) Azure Table Storage 

d) Azure Blob Storage 

7. What is the primary use case for Azure SQL Database?

a) Storing unstructured data 

b) Running real-time analytics 

c) Managing relational data in the cloud 

d) Storing graph data 

8. Which of the following is a feature of Azure SQL Database?

a) Automatic scaling of compute and storage 

b) Schema-less data storage 

c) Support for NoSQL queries 

d) Graph database capabilities 

9. What is the purpose of an index in a relational database?

a) To store unstructured data  

b) To improve query performance 

c) To define relationships between tables 

d) To encrypt data 

10. Which SQL statement is used to retrieve data from a relational database?

a) INSERT  

b) UPDATE 

c) SELECT 

d) DELETE 

Non-Relational Data in Azure

11. Which Azure service is a globally distributed NoSQL database?

a) Azure SQL Database 

b) Azure Cosmos DB 

c) Azure Data Lake Storage 

d) Azure Synapse Analytics 

12. Which of the following is a supported API in Azure Cosmos DB?

a) SQL API 

b) Cassandra API 

c) MongoDB API 

d) All of the above 

13. What is the primary advantage of using Azure Cosmos DB?

a) It only supports relational data 

b) It provides single-region deployment 

c) It offers global distribution and low-latency 

d) It is optimized for batch processing 

14. Which of the following is an example of a key-value store?

a) Azure Table Storage 

b) Azure SQL Database 

c) Azure Data Lake Storage 

d) Azure Synapse Analytics 

15. What is the primary use case for Azure Blob Storage?

a) Storing relational data 

b) Storing unstructured data like images and videos 

c) Running real-time analytics 

d) Managing graph data 

Analytics Workloads in Azure

16. Which Azure service is used for big data analytics?

a) Azure SQL Database 

b) Azure Synapse Analytics 

c) Azure Cosmos DB 

d) Azure Table Storage 

17. What is the primary purpose of Azure Databricks?

a) To store relational data 

b) To provide a collaborative environment for big data analytics 

c) To manage NoSQL databases 

d) To store unstructured data 

18. Which of the following is a feature of Azure Synapse Analytics?

a) Schema-less data storage 

b) Integration with Power BI 

c) Support for NoSQL queries 

d) Graph database capabilities 

19. What is the primary use case for Azure HDInsight?

a) Running real-time analytics 

b) Managing relational databases 

c) Processing big data using open-source frameworks 

d) Storing unstructured data 

20. Which of the following is a benefit of using Azure Data Lake Storage?

a) It only supports structured data 

b) It provides unlimited storage for analytics workloads 

c) It is optimized for transactional workloads 

d) It requires a fixed schema 

Data Integration and Processing

21. Which Azure service is used for ETL (Extract, Transform, Load) processes?

a) Azure Data Factory 

b) Azure SQL Database 

c) Azure Cosmos DB 

d) Azure Blob Storage 

22. What is the primary purpose of Azure Stream Analytics?

a) To store historical data 

b) To process real-time data streams 

c) To manage relational databases 

d) To store unstructured data 

23. Which of the following is a feature of Azure Data Factory?

a) Real-time data processing 

b) Schema-less data storage 

c) Data orchestration and pipeline creation 

d) Graph database capabilities 

24. What is the primary use case for Azure Event Hubs?

a) Storing historical data 

b) Ingesting and processing real-time event data 

c) Managing relational databases 

d) Storing unstructured data 

25. Which of the following is a benefit of using Azure Logic Apps?

a) Real-time data processing 

b) Automated workflow orchestration 

c) Schema-less data storage 

d) Graph database capabilities 

Data Visualization and Reporting

26. Which Azure service is used for data visualization?

a) Azure Synapse Analytics 

b) Power BI 

c) Azure Cosmos DB 

d) Azure Data Lake Storage 

27. What is the primary purpose of Power BI?

a) To store relational data 

b) To create interactive data visualizations and reports 

c) To manage NoSQL databases 

d) To store unstructured data 

28. Which of the following is a feature of Power BI?

a) Real-time data processing 

b) Integration with Azure Synapse Analytics 

c) Schema-less data storage 

d) Graph database capabilities 

29. What is the primary use case for Power BI Desktop?

a) To store historical data 

b) To create and publish reports 

c) To manage relational databases 

d) To store unstructured data 

30. Which of the following is a benefit of using Power BI Service?

a) Real-time data processing 

b) Sharing and collaboration on reports and dashboards 

c) Schema-less data storage 

d) Graph database capabilities 

These questions are designed to help you review the key concepts and services covered in the DP-900 Microsoft Azure Data Fundamentals exam. Good luck with your preparation!