Knowledge + Practice

DP-900Free Study Guide

Microsoft Azure Data Fundamentals DP-900The Complete Beginner's Guide

Complete DP-900 study guide — core data concepts, relational and non-relational data on Azure, analytics workloads.

101 chapters

~42 hours total read

Free — no signup required

How to use this guide

This guide works best as a loop: read a chapter, test yourself with practice questions, look up unfamiliar terms in the glossary, then move to the next chapter.

① Read a chapter② Answer practice questions③ Review missed answers④ Repeat

Study Chapters

101 chapters covering every exam objective. Each chapter includes key concepts, exam tips, common traps, comparison tables, and a 5-question quiz at the end.

Start Chapter 1

Practice Questions

Free timed and untimed practice with instant feedback and full explanations. Pick 10–120 questions per session. Filter by domain to drill your weak areas.

Go to practice test

Glossary

Every DP-900term defined and searchable. Use it when a chapter mentions a concept you haven't seen before or want a quick refresher on.

Browse glossary

Exam Overview

Exam blueprint, domain weights, passing score, duration, cost, and registration links. Start here if you're new to this certification.

View exam guide

Core Data Concepts (25–30%)

27 chapters

Domain overview

Data Roles and Core Concepts

Objective 1.1 · Core Data Concepts

Relational Data Concepts

Objective 1.2 · Core Data Concepts

Non-Relational Data Concepts

Objective 1.3 · Core Data Concepts

Analytics and Data Warehouse Concepts

Objective 1.4 · Core Data Concepts

Data Roles: Engineer, Analyst, Scientist, DBA

Objective 1.1 · Core Data Concepts

ETL vs ELT Data Pipelines

Objective 1.1 · Core Data Concepts

Batch Processing vs Streaming Analytics

Objective 1.4 · Core Data Concepts

Data Lake vs Data Warehouse vs Lakehouse

Objective 1.4 · Core Data Concepts

Microsoft Purview Data Catalog

Objective 1.1 · Core Data Concepts

Data Governance: Quality, Lineage, Cataloguing

Objective 1.1 · Core Data Concepts

Non-Relational DB Types: Document, Key-Value, Graph, Column

Objective 1.3 · Core Data Concepts

Document Databases and JSON Storage

Objective 1.3 · Core Data Concepts

Graph Databases: Gremlin API and Relationships

Objective 1.3 · Core Data Concepts

Key-Value Stores and In-Memory Caching

Objective 1.3 · Core Data Concepts

Column-Family Databases (Cassandra API)

Objective 1.3 · Core Data Concepts

Structured vs Semi-Structured vs Unstructured Data

Objective 1.1 · Core Data Concepts

Data Formats: JSON, CSV, Parquet, and Avro

Objective 1.1 · Core Data Concepts

OLAP vs OLTP Workloads

Objective 1.4 · Core Data Concepts

Star Schema vs Snowflake Schema

Objective 1.4 · Core Data Concepts

Data Governance: Lineage, Glossary, and Classification

Objective 1.1 · Core Data Concepts

Microsoft Purview Data Map and Scanning

Objective 1.1 · Core Data Concepts

GDPR and Data Privacy on Azure

Objective 1.1 · Core Data Concepts

Data Ingestion Patterns: Batch vs Streaming

Objective 1.4 · Core Data Concepts

Schema-on-Read vs Schema-on-Write

Objective 1.1 · Core Data Concepts

Data Mesh and Domain-Oriented Data Ownership

Objective 1.1 · Core Data Concepts

Data Quality: Completeness, Accuracy, Consistency

Objective 1.1 · Core Data Concepts

Master Data Management Concepts

Objective 1.1 · Core Data Concepts

Relational Data in Azure (45–50%)

32 chapters

Domain overview

Azure SQL Services

Objective 2.1 · Relational Data

Azure SQL Managed Instance and SQL VM

Objective 2.2 · Relational Data

SQL Querying Basics

Objective 2.3 · Relational Data

Azure Cosmos DB

Objective 2.4 · Relational Data

Azure Table Storage and Blob Data

Objective 2.5 · Relational Data

Database Normalization (1NF, 2NF, 3NF)

Objective 2.1 · Relational Data

SQL Joins: INNER, LEFT, RIGHT, FULL

Objective 2.3 · Relational Data

Indexes, Views, and Stored Procedures

Objective 2.3 · Relational Data

Azure SQL Hyperscale and Serverless

Objective 2.1 · Relational Data

Azure SQL Elastic Pools

Objective 2.2 · Relational Data

Cosmos DB APIs: Core SQL, MongoDB, Cassandra, Gremlin

Objective 2.4 · Relational Data

Cosmos DB Consistency Levels

Objective 2.4 · Relational Data

ACID Properties in Relational Databases

Objective 2.1 · Relational Data

SQL DML and DDL: SELECT, INSERT, CREATE, ALTER

Objective 2.3 · Relational Data

SQL Data Types and Constraints

Objective 2.3 · Relational Data

Full-Text Search in Azure SQL

Objective 2.3 · Relational Data

Azure SQL Authentication: SQL vs Entra ID

Objective 2.1 · Relational Data

Transparent Data Encryption (TDE) in Azure SQL

Objective 2.1 · Relational Data

Azure SQL Active Geo-Replication

Objective 2.2 · Relational Data

Azure SQL Pricing: DTU vs vCore Models

Objective 2.1 · Relational Data

Azure Database for MySQL

Objective 2.1 · Relational Data

Azure Database for PostgreSQL

Objective 2.1 · Relational Data

Row-Level Security in Azure SQL

Objective 2.1 · Relational Data

Temporal Tables in Azure SQL

Objective 2.3 · Relational Data

Cosmos DB Partitioning Strategies

Objective 2.4 · Relational Data

Cosmos DB Request Units (RU/s)

Objective 2.4 · Relational Data

Cosmos DB Global Distribution and Failover

Objective 2.4 · Relational Data

Cosmos DB Change Feed for Streaming

Objective 2.4 · Relational Data

Data Security: Encryption, Masking, and Anonymisation

Objective 2.1 · Relational Data

Azure Cache for Redis for Low-Latency Data

Objective 2.5 · Relational Data

Azure SQL Managed Instance Deep Dive

Objective 2.2 · Relational Data

Azure SQL Elastic Pool Cost Optimisation

Objective 2.2 · Relational Data

Analytics Workloads on Azure (25–30%)

42 chapters

Domain overview

Azure Synapse Analytics

Objective 3.1 · Analytics

Azure Data Factory

Objective 3.2 · Analytics

Azure Databricks

Objective 3.3 · Analytics

Power BI Fundamentals

Objective 3.4 · Analytics

Real-Time Analytics on Azure

Objective 3.5 · Analytics

Azure HDInsight

Objective 3.1 · Analytics

Azure Data Lake Storage Gen2

Objective 3.1 · Analytics

Azure Stream Analytics

Objective 3.5 · Analytics

Microsoft Fabric Overview

Objective 3.1 · Analytics

Power BI: Reports vs Dashboards vs Datasets

Objective 3.4 · Analytics

Power BI Gateway for On-Premises Data

Objective 3.4 · Analytics

Data Warehouse Concepts: Facts and Dimensions

Objective 3.1 · Analytics

Synapse Dedicated vs Serverless SQL Pools

Objective 3.1 · Analytics

Azure Synapse Spark Pools

Objective 3.1 · Analytics

Azure Synapse Link for Cosmos DB

Objective 3.1 · Analytics

Delta Lake Format in Azure

Objective 3.1 · Analytics

Bronze, Silver, Gold Medallion Architecture

Objective 3.1 · Analytics

Azure Data Factory Activities and Pipelines

Objective 3.2 · Analytics

ADF Triggers: Scheduled, Tumbling, Event-Based

Objective 3.2 · Analytics

Databricks Notebooks and Clusters

Objective 3.3 · Analytics

Databricks Delta Live Tables

Objective 3.3 · Analytics

Power BI Datasets and Dataflows

Objective 3.4 · Analytics

DAX Formulas and Measures in Power BI

Objective 3.4 · Analytics

Power BI Service vs Power BI Desktop

Objective 3.4 · Analytics

Row-Level Security in Power BI

Objective 3.4 · Analytics

Azure Event Hubs for Streaming Data

Objective 3.5 · Analytics

Stream Analytics SQL Queries and Windows

Objective 3.5 · Analytics

Lambda and Kappa Data Architectures

Objective 3.5 · Analytics

Data Lakehouse vs Data Warehouse vs Data Lake

Objective 3.1 · Analytics

Azure Data Lake Storage Gen2 Hierarchical Namespace

Objective 3.1 · Analytics

HDInsight Hadoop and MapReduce

Objective 3.1 · Analytics

Microsoft Fabric: OneLake and Workspaces

Objective 3.1 · Analytics

Azure Data Share for Sharing Datasets

Objective 3.2 · Analytics

Azure AI Search for Unstructured Data

Objective 3.5 · Analytics

Search Indexes and Full-Text Search Concepts

Objective 3.5 · Analytics

BigQuery vs Azure Synapse Analytics

Objective 3.1 · Analytics

Power BI Premium vs Pro Licensing

Objective 3.4 · Analytics

Azure Analysis Services

Objective 3.4 · Analytics

PolyBase and External Tables in Synapse

Objective 3.1 · Analytics

Azure IoT Hub and Telemetry Ingestion

Objective 3.5 · Analytics

Apache Spark Core Concepts for DP-900

Objective 3.3 · Analytics

Data Warehouse Distribution: Hash, Round-Robin, Replicated

Objective 3.1 · Analytics

Start Chapter 1

Ready to test your knowledge?

Free DP-900 practice questions with full explanations. Test what you learn chapter by chapter.

DP-900 Practice Questions