Microsoft Certified: Azure Data Fundamentals (DP-900)

Microsoft Certified: Azure Data Fundamentals (DP-900)

Overview

The Microsoft Certified: Azure Data Fundamentals certification validates your baseline knowledge of core data concepts and how they are implemented using Microsoft Azure data services. Designed as a foundational credential, the DP-900 exam serves as an entry point for professionals looking to build a career in cloud data management, analytics, and business intelligence. By earning this credential, candidates demonstrate a solid grasp of relational and non-relational data systems, data ingestion, processing, and visualization in the cloud.

Whether you are an aspiring data professional or an established IT practitioner transitioning to Azure, passing the DP-900 exam confirms your comprehension of standard data principles and introduces you to key cloud services such as Azure SQL Database, Azure Cosmos DB, Azure Synapse Analytics, and Microsoft Power BI.

Benefits

• Validated Fundamental Knowledge: Prove your mastery of essential cloud data architectures, storage types, and processing techniques on Microsoft Azure.
• Stepping Stone to Advanced Certifications: Build a robust baseline before pursuing role-based credentials such as Azure Data Engineer Associate (DP-203) or Azure Database Administrator Associate (DP-300).
• Enhanced Employability: Stand out to recruiters looking for cloud-literate professionals capable of discussing and applying modern cloud data strategies.
• Vendor-Backed Credibility: Obtain an industry-recognized certification directly from Microsoft, signaling commitment to continuous professional development.
• Cross-Functional Fluency: Bridge the gap between business stakeholders and technical teams by understanding how cloud data solutions solve organizational challenges.

Who should take this exam

• Beginner Data Professionals: Individuals seeking to begin a career in database administration, data engineering, or cloud data analytics.
• Cloud Architects and IT Administrators: Professionals aiming to expand their knowledge of Azure's managed database and analytics services.
• Software Developers: Engineers looking to understand relational and non-relational database options available across the Azure ecosystem.
• Business Analysts and Decision Makers: Non-technical stakeholders who evaluate data storage, processing, and reporting options on the cloud.
• Students and Career Switchers: Anyone wishing to validate foundational knowledge of data management concepts on modern cloud platforms.

Prerequisites

There are no mandatory technical prerequisites required to take the DP-900 exam. However, candidates will benefit from:

• A foundational understanding of general technology and computer concepts.
• Basic familiarity with relational databases, non-relational data, or spreadsheet data analysis.
• General awareness of cloud computing principles and core Azure services.

Learning outcomes

• Understand Core Data Concepts: Define structured, semi-structured, and unstructured data, batch versus streaming processing, and transactional versus analytical workloads.
• Assess Relational Data Services: Identify core relational concepts, normalization, and relational offerings in Azure including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure VMs.
• Evaluate Non-Relational Workloads: Explain characteristics of non-relational storage including Azure Blob Storage, Azure Files, Azure Data Lake Storage Gen2, and Azure Cosmos DB.
• Explore Analytics Workloads: Describe data warehousing, real-time analytics with Azure Stream Analytics and Azure Event Hubs, and enterprise reporting using Power BI.
• Understand Data Security and Governance: Identify fundamental security controls, authentication mechanisms, and data governance practices across Azure data services.

Career opportunities

• Junior Cloud Data Engineer: Assist in building data pipelines, configuring data storage, and ingesting streaming data into Azure.
• Junior Database Administrator: Support the deployment, maintenance, and monitoring of relational and NoSQL database instances in cloud environments.
• Data Analytics Specialist: Assist business units with data modeling, dashboard creation, and visual reporting using Microsoft Power BI and Azure Synapse.
• Cloud Solutions Consultant: Advise clients and internal teams on data modernization strategies and cloud migration paths.
• Technical Support Engineer: Provide tier-one support for applications backed by Azure data services.

Exam syllabus

Describe core data concepts (25–30%)

• Identify ways to represent data: Differentiate between structured, semi-structured, and unstructured data formats like JSON, CSV, Parquet, and Avro.
• Identify options for data storage: Differentiate between file storage, relational databases, document stores, and key-value stores.
• Identify common data workloads: Contrast transactional (OLTP) workloads with analytical (OLAP) workloads.
• Identify roles and responsibilities: Describe data-related responsibilities including Database Administrators, Data Engineers, and Data Analysts.

Identify considerations for relational data on Azure (20–25%)

• Describe relational concepts: Understand tables, columns, rows, primary keys, foreign keys, indexes, and normalization principles.
• Identify relational data offerings in Azure: Compare Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines.
• Describe open-source database services: Explore managed services like Azure Database for PostgreSQL and Azure Database for MySQL.
• Identify basic management tasks: Understand provisioning, connectivity, firewall rules, and basic backup configurations.

Describe considerations for working with non-relational data on Azure (15–20%)

• Describe non-relational data storage types: Understand key-value, document, column-family, and graph data models.
• Identify Azure non-relational data offerings: Explain Azure Blob Storage, Azure Data Lake Storage Gen2, Azure Files, and Azure Table Storage.
• Explore Azure Cosmos DB: Describe the globally distributed architecture, APIs (Core SQL, MongoDB, Cassandra, Gremlin), and consistency levels.

Describe an analytics workload on Azure (25–30%)

• Identify data warehousing components: Explore modern data warehousing patterns using Azure Synapse Analytics and Azure Databricks.
• Describe data ingestion and processing: Understand extract, transform, load (ETL) and extract, load, transform (ELT) pipelines using Azure Data Factory.
• Describe real-time data processing: Understand real-time ingestion and processing using Azure Event Hubs, Azure IoT Hub, and Azure Stream Analytics.
• Describe data visualization: Understand reporting, dashboards, data modeling, and security using Microsoft Power BI.