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Prepping for the Microsoft Azure Data Fundamentals Exam

  • How to prepare for Microsoft Azure data Fundamentals exam?
  • Published by: André Hammer on Feb 02, 2024

If you're thinking about taking the Azure Data Fundamentals exam, it's important to prepare. With the growing need for data professionals, getting this certification can boost your career.

In this article, we'll share tips and resources to help you succeed in the Azure Data Fundamentals exam. Whether you're new to Azure or want to validate your current knowledge, these insights will point you in the right direction.

Understanding the DP-900 Exam Structure

Skills Measured

The Microsoft Azure Data Fundamentals Exam covers core data concepts. These include understanding relational data, non-relational data, and analytics solutions. Candidates are also expected to have knowledge of basic data processing, data governance, and data security concepts.

The exam measures skills related to Azure data services, including Azure SQL Database, Azure Cosmos DB, Azure Synapse Analytics, and Azure Data Lake Storage.

For relational concepts, candidates should understand database concepts such as tables, rows, and columns. They should also have knowledge of SQL and database design.

Non-relational concepts covered in the exam include knowledge of NoSQL databases, types of non-relational data, and non-relational data workloads.

Understanding the core data concepts and Azure data services measured in the exam is essential to adequately prepare for the Microsoft Azure Data Fundamentals certification.

Audience Profile

The Microsoft Azure Data Fundamentals exam is for people in finance, healthcare, retail, and manufacturing. They might work in data analysis, business intelligence, or database administration. Some are new to data and analytics, while others have a few years of experience. Everyone taking the exam wants to understand data concepts and how to work with data on the Azure platform.

Roles and Responsibilities

A data management or data analysis team typically includes data engineers and data analysts.

Data engineers design and build scalable data models, while data analysts interpret complex data sets.

Team members collaborate through regular meetings and updates, using project management tools like Jira or Trello to track progress.

Effective communication is important for data engineers to understand analysts' requirements, and for analysts to know platform capabilities and limitations.

Clear data governance policies ensure that each team member's role aligns with project objectives.

Identify Core Data Concepts

Data Workloads

Understanding different types of data workloads is important for managing and analysing data effectively. There are three main types of data workloads: transactional, analytical, and ETL (Extract, Transform, Load).

Transactional workloads involve real-time processing and focus on individual data interactions. Analytical workloads, on the other hand, deal with processing large volumes of data for reporting and analysis. ETL workloads involve the extraction, transformation, and loading of data.

When dealing with these workloads, it's important to consider factors such as the scalability of data storage and processing systems, security and privacy implications, and performance requirements. By considering these factors, individuals can ensure that their data management and analysis efforts are effective and efficient.

Relational Data

Relational data is a type of data that organises relationships and connections between different data points in a structured way. To prepare for the Microsoft Azure Data Fundamentals exam, understanding key concepts related to relational data is important.

This includes tables, rows, columns, and primary and foreign keys. Microsoft Azure provides Azure SQL Database and Azure SQL Managed Instance for storing and managing relational data. These databases fit into relational data concepts, offering scalability, security, and performance in the cloud.

On the other hand, non-relational data stores, or NoSQL databases, provide different data storage and retrieval options compared to relational databases. Examples of non-relational data stores include document-oriented, key-value, columnar, and graph databases, each with their own characteristics and use cases.

Successfully preparing for the Microsoft Azure Data Fundamentals exam requires an understanding of the differences between relational and non-relational data stores.

Non-Relational Data

Non-relational data stores can handle large volumes of unstructured data, making them flexible and scalable. These include document databases, key-value stores, wide-column stores, and graph databases.

Unlike relational databases, they do not need a fixed schema and can easily accommodate changes to the data structure.

Non-relational data stores typically use horizontal scaling for storage and retrieval, efficiently distributing data across multiple servers. This makes them ideal for big data and real-time applications.

They also use denormalization, reducing the need for complex joins and allowing for faster data access.

Understanding these characteristics and differences is important for success in the Microsoft Azure data fundamentals exam.

Explore Azure Data Services

Azure Storage

Azure Storage offers different storage solutions for all types and sizes of data. These include file, object, and queue storage. It provides features like encryption for data at rest and in transit, and supports various data workloads, both relational and non-relational.

For instance, Azure Blob Storage is highly scalable and ideal for storing and retrieving large amounts of unstructured data, such as documents or media files.

To manage and secure data in Azure Storage, best practices include using role-based access control and secure transfer protocols. It's also important to regularly monitor and audit data access and usage to ensure data security and compliance with industry standards.

Azure Cosmos DB

Azure Cosmos DB offers several key features that make it a great choice for enterprise applications. Features like global distribution, multiple consistency models, and industry-leading SLAs provide reliable data storage and management. This allows for seamless scalability, high availability, and low latency, making it ideal for high-traffic and data processing requirements.

Compared to other data storage solutions in Azure Data Services, Azure Cosmos DB stands out for its seamless integration with various Azure services, including Azure Functions and Azure Logic Apps. This makes it easy for developers to build and deploy robust, fully managed applications.

Common use cases for Azure Cosmos DB in enterprise applications include real-time analytics, personalized content delivery, and IoT solutions. These applications require high performance, reliability, and scalability in their data management.

Azure Data Lake

Azure Data Lake consists of three components: Data Lake Store, Data Lake Analytics, and HDInsight.

Data Lake Store is for storing and managing various data types. This includes structured, semi-structured, and unstructured data.

Data Lake Analytics is for big data analytics and processing.

HDInsight is a cloud service for processing massive amounts of data using open-source frameworks like Hadoop, Spark, and Hive.

Azure Data Lake features unlimited storage, high performance, scalability, secure data access controls, and the ability to handle large amounts of requests and analytics jobs.

Compared to other data storage solutions in Azure, Azure Data Lake provides a comprehensive set of big data capabilities. It's suitable for organizations dealing with complex and diverse data types, needing sophisticated data processing and analytics capabilities.

Discover Relational Concepts

Relational Concepts

Understanding relational concepts is important for the Microsoft Azure data fundamentals exam. These concepts include primary keys, foreign keys, and normalization.

Relational data is structured in tables with rows and columns, while non-relational data is more flexible and stored in various formats like documents and graphs.

Azure SQL Database is a fully managed cloud database, while Azure SQL Managed Instance is a platform-as-a-service (PaaS) database engine.

It's important to grasp the variances between these two offerings before the exam.

Azure SQL Database

Azure SQL Database is a managed relational database service by Microsoft Azure. It stores and manages data within Azure.

It offers automatic database tuning, intelligence, and advanced security. Using it improves performance, availability, and scalability.

Compared to other Azure relational data concepts, it's a Platform as a Service offering. This makes managing relational databases simpler and cost-effective.

Its seamless integration with other Azure services simplifies data management for organisations.

Azure SQL Managed Instance

Azure SQL Managed Instance is a fully-managed database service with built-in intelligence. It offers greater flexibility and cost savings.

Key benefits include automatic backups, high availability, and reduced management overhead.

Azure SQL Database is designed for modern cloud applications with features like elastic scaling. Azure SQL Managed Instance eases migration from on-premises SQL Server with near 100% compatibility.

Best practices for migration involve using the Database Migration Service for a seamless transition, conducting thorough performance testing, and ensuring compatibility with any third-party applications.

Dive into Non-Relational Concepts

Key-Value Data Store

A Key-Value Data Store is a type of NoSQL database. It stores data as key-value pairs in a schema-less way. Unlike traditional SQL databases, it doesn't have a fixed schema. This allows for more flexibility in data storage and retrieval.

The key is a unique identifier for the data. The value is the actual data being stored. This storage solution is ideal for simple data models with a need for high scalability.

Key-Value Data Stores offer benefits such as high performance, scalability, and fault tolerance. They are also easy to use and handle large volumes of data efficiently. These features are important for managing and processing data.

Distributed cloud-based services can effectively implement Key-Value Data Stores. They provide seamless scalability and high availability.

Real-world scenarios like e-commerce platforms can efficiently manage large amounts of user and product data using Key-Value Data Stores. In data management systems, it offers a flexible and scalable solution for handling large volumes of data.

Document Store

A document store is great for storing unstructured data like JSON, XML, or BSON documents. It's flexible and scalable, allowing real-time retrieval of information. This is helpful for applications that need quick access to a lot of data.

Unlike relational databases, which use structured data and complex joins, document stores have more natural and intuitive storage and retrieval. They're commonly used in content management systems, e-commerce platforms, and mobile applications where efficient and flexible data storage is important.

With the ability to handle semi-structured and unstructured data, a document store is a valuable addition to any data management system, especially for organisations wanting to benefit from NoSQL databases and meet the diverse data needs of modern applications.

Column Family Store

A column family store is a type of NoSQL database. It organizes data into columns instead of rows, making it efficient for analytics and data management.

Key characteristics:

  • Efficient for handling large amounts of data
  • Provides quick read access for analytical queries

In contrast to key-value or document stores, column family stores are optimized for querying and analyzing large datasets. They are suitable for applications that require complex data analysis and reporting, with common use cases including real-time analytics, time-series data, and data warehousing.

Benefits of using a column family store:

  • Scalability
  • High performance
  • Flexibility in data modeling

For those preparing for the Microsoft Azure Data Fundamentals exam, understanding the characteristics and use cases of a column family store is important for demonstrating knowledge of NoSQL databases and data management principles.

Investigate Large-Scale Analytics

Big Data and Analytics Workload

Preparing for the Microsoft Azure data Fundamentals exam involves understanding Big Data and Analytics Workload. This includes grasping concepts such as data processing, data storage, data movement, and orchestration. It's crucial to gain knowledge of tools like Azure Synapse Analytics and Azure Databricks for handling Big Data and Analytics Workload. These tools provide integrated capabilities for big data and data warehousing.

Understanding different types of data workloads, like batch, interactive, streaming, and machine learning workloads, is essential for comprehending their impact. Each type of workload has different requirements and characteristics, which must be considered in the context of Big Data and Analytics Workload.

Azure Synapse Analytics

Azure Synapse Analytics makes it easy to handle big data and data analytics. It combines data warehousing and big data processing in one platform. Users can query structured and unstructured data using on-demand or provisioned resources. It seamlessly works with other Azure data services like Azure Data Lake and Azure Cosmos DB, making it simple to ingest, process, and analyse large volumes of data in one place.

Common uses include building data warehouses, real-time analytics, and advanced analytics and machine learning. It's designed to optimize performance, minimize data movement, and simplify management, providing scalable and cost-effective solutions.

Azure Databricks

Azure Databricks has important features for large-scale analytics. It includes a platform for traditional and machine learning workloads, a workspace for data scientists and engineers to work together, and real-time data analytics. It can integrate with different data sources and has built-in support for modern data warehousing solutions.

When adding Azure Databricks to a data storage and analytics solution, it's vital to think about data security, compliance, and scalability. Also, consider how easily it can integrate with existing data infrastructure and if it has the tools for exploring and visualizing data.

Analyse Real-Time Data Analytics

Real-time data analytics is a powerful tool for making immediate business decisions. It provides insights into customer behaviour, market trends, and operational performance. By processing and analyzing data as it is generated, organizations can identify opportunities and threats in real-time. This allows for quick and informed decision-making.

Key technologies and tools used in real-time data analytics include:

  • Apache Kafka for real-time data streaming
  • Apache Spark for real-time data processing
  • Apache Flink for real-time data analytics and machine learning

Implementing real-time data analytics offers the advantage of gaining a competitive edge through faster decision-making, improved customer satisfaction, and enhanced operational efficiency. However, challenges such as data accuracy, scalability, and the need for specialized skills can arise during implementation.

Discuss Data Storage Solutions

Businesses often use different data storage solutions like cloud-based storage, network-attached storage (NAS), and storage area network (SAN). These options vary in cost, scalability, and performance.

For instance, cloud-based storage is scalable and cost-effective, making it a good choice for small to medium-sized businesses. On the other hand, a SAN solution provides high performance but comes with a higher price.

When selecting a data storage solution, businesses should consider factors such as data volume, performance needs, and budget. For example, if high-performance storage for a large volume of data is needed, a SAN solution may be best. If budget and scalability are priorities, cloud-based storage could be ideal.

Considering these factors is important for finding the most suitable data storage solution for a business.

Comprehend Data Visualisation Techniques

Understanding data visualisation techniques is important for success in the Microsoft Azure Data Fundamentals exam. Some common techniques include:

  • Bar graphs
  • Line charts
  • Scatter plots
  • Pie charts

How to Prepare for Microsoft Azure Data Fundamentals Exam?

To excel in the Microsoft Azure Data Fundamentals Exam (DP-900), candidates should have a good understanding of important data concepts. These include various data types, relational and non-relational data, and data workloads. It's also important to be familiar with core Azure data services like Azure SQL Database, Azure Cosmos DB, Azure Synapse Analytics, and Azure Data Factory.

A great way to explore and understand these services is by using Microsoft Learn. This platform offers hands-on training modules, interactive experiences, and testing labs. Candidates can use these resources to gain a thorough understanding of the features and capabilities of each Azure data service. This will ensure they are well-prepared for the exam.


Get ready for the Azure Data Fundamentals Exam. Start by getting to know the core concepts and services for data storage and processing in the Azure cloud. Understand the key features and functionalities of Azure data services. Practice using these services to gain practical experience. Also, check out the exam objectives and study resources provided by Microsoft for comprehensive preparation.

Readynez offers a 1-day DP-900 Azure Data Fundamentals Course and Certification Program, providing you with all the learning and support you need to successfully prepare for the exam and certification. The DP-900 Azure Data Fundamentals course, and all our other Microsoft Azure courses, are also included in our unique Unlimited Microsoft Training offer, where you can attend the Azure Data Fundamentals and 60+ other Microsoft courses for just €199 per month, the most flexible and affordable way to get your Microsoft Certifications.

Please reach out to us with any questions or if you would like a chat about your opportunity with the Azure Data Fundamentals certification and how you best achieve it. 


What topics should I focus on while prepping for the Azure Data Fundamentals Exam?

Focus on topics such as core data concepts, data storage, data processing, and data visualization techniques. Understand Azure data services such as Azure SQL Database, Azure Cosmos DB, Azure Data Lake Storage, and Azure Synapse Analytics. Practice with hands-on exercises and review sample exam questions.

What study resources are available for the Azure Data Fundamentals Exam?

Study resources for the Azure Data Fundamentals Exam include Microsoft Learn modules, official practice tests, exam preparation guides, and online training courses from platforms like Coursera and Udemy.

Are there any practice exams or sample questions for the Azure Data Fundamentals Exam?

Yes, there are practice exams and sample questions available for the Azure Data Fundamentals Exam on the official Microsoft website and other online platforms like Udemy and Coursera.

What is the exam format for the Azure Data Fundamentals Exam?

The exam format for the Azure Data Fundamentals Exam includes multiple-choice questions and case studies. For example, you may be asked to identify the best data storage solution for a given scenario.

Are there any specific prerequisites for taking the Azure Data Fundamentals Exam?

No, there are no specific prerequisites for taking the Azure Data Fundamentals Exam.

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