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DP-900
DP-900 Microsoft Azure

Data Fundamentals

The DP-900 course teaches core data concepts, relational and non‑relational data on Azure, and analytics workloads, enabling learners to understand and evaluate Azure data services for business solutions.

85
Minutes
50
Questions
700/1000
Passing Score
$99
Exam Cost
10
Languages

Who Should Take This

It is ideal for aspiring data professionals, business analysts, and IT staff who have basic cloud familiarity and want to validate their understanding of Azure data fundamentals. By completing the exam, they can earn the Microsoft Certified: Azure Data Fundamentals credential, demonstrating readiness to support data‑driven initiatives and advance toward higher‑level Azure certifications.

What's Covered

1 Foundational data concepts including data formats, data stores, transactional and analytical workloads, and roles in data management.
2 Relational database concepts including normalization, SQL, Azure SQL Database, Azure SQL Managed Instance, and Azure Database for open-source engines.
3 Non-relational data stores including Azure Cosmos DB, Azure Table Storage, Azure Blob Storage, and Azure Files.
4 Analytics concepts including data warehousing, real-time analytics, data visualization with Power BI, Azure Synapse Analytics, and Azure Data Factory.

Exam Structure

Question Types

  • Multiple Choice
  • Multiple Response
  • Drag-And-Drop

Scoring Method

Scaled score 100-1000, passing score 700

Delivery Method

Proctored exam, 40-60 questions, 45 minutes

Recertification

Fundamentals certifications do not expire

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats

Course Outline

76 learning goals
1 Domain 1: Describe Core Data Concepts
5 topics

Describe ways to represent data

  • Describe structured data formats including tables with rows and columns, fixed schemas, and how structured data maps to relational database storage.
  • Describe semi-structured data formats including JSON, XML, and key-value pairs and explain how flexible schemas differ from rigid tabular structures.
  • Describe unstructured data types including images, audio, video, and free-form text and explain why these data types require specialized storage and processing approaches.
  • Differentiate between structured, semi-structured, and unstructured data by identifying the appropriate data category for a given real-world data scenario and recommending a suitable storage approach.

Identify options for data storage

  • Explain how file-based data storage formats including delimited files, JSON, XML, Avro, ORC, and Parquet differ in schema enforcement, compression, and query performance characteristics.
  • Describe database storage options including relational databases, document databases, key-value stores, column-family databases, and graph databases at a conceptual level.
  • Evaluate data storage options by analyzing data structure, query requirements, scalability needs, and consistency models to determine the most appropriate storage approach for a given scenario.

Describe common data workload types

  • Describe transactional data workloads and explain ACID properties, high-concurrency write patterns, and the role of OLTP systems in operational applications.
  • Describe analytical data workloads and explain read-optimized schemas, aggregation queries, multidimensional modeling, and the role of OLAP systems in decision support.
  • Explain the differences between batch data processing and streaming data processing by comparing latency, throughput, scheduling, and use cases for each approach.
  • Analyze a data workload scenario to determine whether OLTP, OLAP, or a combination is required based on the application's transactional needs, analytical requirements, and data freshness constraints.

Describe data analytics concepts

  • Describe descriptive analytics and explain how it uses historical data to answer what happened through aggregation, summarization, and reporting.
  • Explain how diagnostic, predictive, and prescriptive analytics progressively advance from identifying root causes to forecasting outcomes to recommending optimal actions using data-driven models.
  • Evaluate a business analytics scenario to determine the appropriate analytics type by assessing whether the question requires descriptive, diagnostic, predictive, or prescriptive analysis.

Identify roles and responsibilities for data workloads

  • Describe the database administrator role and explain responsibilities including provisioning, securing, monitoring, and optimizing database performance and availability.
  • Describe the data engineer role and explain responsibilities including building data pipelines, transforming data, integrating sources, and ensuring data quality for downstream consumption.
  • Describe the data analyst role and explain responsibilities including exploring data, creating visualizations, building reports, and communicating insights to business stakeholders.
  • Assess a data workload task description to determine the appropriate role by distinguishing whether the activity requires database administration, data engineering, or data analysis skills.
2 Domain 2: Identify Considerations for Relational Data on Azure
3 topics

Describe relational concepts

  • Describe relational database tables including rows, columns, data types, and how tables store entities with defined attributes in a structured format.
  • Explain how primary keys, foreign keys, and composite keys establish unique row identification and referential integrity between related tables in a relational database schema.
  • Explain database normalization including first, second, and third normal forms and demonstrate how normalization reduces data redundancy and improves data integrity in a given schema.
  • Explain how indexes improve query performance by enabling efficient data retrieval and describe the tradeoffs between read performance gains and write overhead.
  • Explain how views as virtual tables created from SELECT queries simplify complex queries, restrict data access, and present aggregated data from multiple tables for reporting purposes.
  • Evaluate a data storage requirement to determine the appropriate combination of normalization level, key design, index strategy, and view definitions for a relational database schema.

Describe relational Azure data services

  • Describe Azure SQL Database as a fully managed PaaS relational database and explain its serverless and provisioned compute tiers, elastic pools, and built-in high availability.
  • Explain how Azure SQL Managed Instance provides near-complete SQL Server compatibility with managed infrastructure for lift-and-shift migration scenarios requiring features unavailable in Azure SQL Database.
  • Explain how SQL Server on Azure Virtual Machines provides full SQL Server control with IaaS flexibility for workloads requiring OS-level access or features unsupported by managed offerings.
  • Describe Azure Database for PostgreSQL and its Flexible Server deployment option for open-source relational workloads requiring PostgreSQL engine compatibility.
  • Describe Azure Database for MySQL and Azure Database for MariaDB as managed open-source relational database services with built-in high availability and automated backups.
  • Evaluate Azure SQL deployment options by comparing management overhead, compatibility requirements, migration complexity, and cost to determine the appropriate SQL service for a given scenario.
  • Analyze a relational database workload to choose the most appropriate Azure relational service by evaluating engine compatibility, management model, scalability needs, and open-source requirements.

Identify basic management tasks for relational data

  • Explain how to provision and deploy Azure SQL Database resources including selecting service tiers, configuring compute sizes, and choosing between single database and elastic pool models.
  • Explain how to secure Azure relational databases using firewall rules, virtual network service endpoints, Microsoft Entra authentication, and transparent data encryption.
  • Describe backup, restore, and geo-replication capabilities for Azure relational databases and explain how they support disaster recovery and business continuity requirements.
  • Analyze a database management scenario to recommend the appropriate combination of provisioning, security, and disaster recovery configurations for an Azure relational database deployment.
3 Domain 3: Describe Considerations for Working with Non-Relational Data on Azure
3 topics

Identify types of NoSQL databases

  • Describe document data stores and explain how they store self-describing JSON documents with flexible schemas for content management and catalog applications.
  • Describe key-value data stores and explain how they use simple key-based lookups for high-throughput caching, session management, and configuration storage scenarios.
  • Describe column-family data stores and explain how they organize data into column families for wide-row storage patterns optimized for large-scale analytical and IoT workloads.
  • Describe graph data stores and explain how they use nodes, edges, and properties to represent and traverse complex relationships in social networks and recommendation engines.
  • Evaluate a data scenario to determine the appropriate NoSQL data model by comparing document, key-value, column-family, and graph options based on data structure, access patterns, and performance requirements.

Describe Azure Cosmos DB

  • Describe Azure Cosmos DB as a globally distributed multi-model database service and explain its guaranteed low-latency, turnkey global distribution, and elastic scalability.
  • Explain how each Cosmos DB API option including NoSQL, MongoDB, Cassandra, Gremlin, and Table maps to a specific non-relational data model and supports different application migration paths.
  • Explain Cosmos DB consistency levels including strong, bounded staleness, session, consistent prefix, and eventual and demonstrate how to choose a level based on consistency versus performance tradeoffs.
  • Explain Cosmos DB request units as the currency for throughput provisioning and demonstrate how partition key selection affects data distribution, query efficiency, and cost optimization.
  • Analyze an application scenario to choose the appropriate Cosmos DB API and consistency level by evaluating data model requirements, latency constraints, and migration considerations.

Describe Azure non-relational data offerings

  • Explain Azure Blob Storage blob types including block, append, and page blobs and demonstrate how access tiers including hot, cool, cold, and archive optimize storage costs for different access patterns.
  • Explain how Azure Table Storage provides a key-value store for semi-structured data using partition keys and row keys and compare it with the Cosmos DB Table API for workload migration.
  • Explain how Azure Files provides fully managed file shares accessible via SMB and NFS protocols and demonstrate its use for cloud and on-premises hybrid file sharing scenarios.
  • Explain how Azure Data Lake Storage Gen2 combines hierarchical namespace with blob storage scalability for big data analytics workloads requiring fine-grained access control.
  • Analyze a non-relational data storage scenario to determine whether Azure Blob Storage, Azure Table Storage, Azure Files, Azure Data Lake Storage, or Azure Cosmos DB best meets requirements based on data model, scale, and access patterns.
4 Domain 4: Describe an Analytics Workload on Azure
5 topics

Describe common elements of large-scale analytics

  • Describe data warehousing concepts including star and snowflake schemas, fact tables, dimension tables, and how warehouses optimize complex analytical queries over historical data.
  • Explain how data lake architecture stores raw data in native formats at scale and demonstrate its use for flexible exploration, machine learning, and batch analytics workloads.
  • Explain the data lakehouse architecture and demonstrate how it combines the schema enforcement and query performance of data warehouses with the flexibility and scale of data lakes.
  • Explain the ELT and ETL data ingestion patterns and demonstrate how extract-load-transform differs from extract-transform-load in terms of processing location, use cases, and tool requirements.
  • Describe batch processing and streaming processing in the context of analytics and explain how windowing, micro-batching, and event-time processing handle real-time data ingestion.
  • Compare data warehouse, data lake, and data lakehouse architectures by evaluating schema flexibility, query performance, data governance, and cost characteristics for a given analytics scenario.

Describe Azure data analytics services

  • Describe Azure Synapse Analytics and explain how it provides an integrated analytics workspace combining dedicated SQL pools, serverless SQL, Spark pools, and data integration pipelines.
  • Explain how Azure Synapse dedicated SQL pools and serverless SQL pools serve different analytics workloads and demonstrate when to use provisioned versus on-demand compute for query processing.
  • Describe Azure Databricks and explain how it provides a collaborative Apache Spark-based analytics platform for data engineering, data science, and machine learning workloads.
  • Describe Azure HDInsight and explain how it provides managed open-source analytics clusters for Hadoop, Spark, Hive, and Kafka workloads requiring direct framework control.
  • Explain how Azure Data Factory provides cloud-based data integration for creating, scheduling, and orchestrating ETL and ELT data pipelines connecting diverse data sources at scale.
  • Describe Microsoft Fabric and explain how it provides a unified SaaS analytics platform combining data integration, data engineering, data warehousing, data science, and business intelligence capabilities.
  • Evaluate Azure Synapse Analytics, Azure Databricks, and Microsoft Fabric by comparing integration capabilities, workload specialization, collaboration features, and licensing to determine the best fit for analytics scenarios.
  • Analyze an analytics workload scenario to select the most appropriate combination of Azure analytics services based on data volume, processing requirements, team skills, and integration needs.

Describe real-time and streaming analytics on Azure

  • Explain how Azure Stream Analytics provides real-time event processing using SQL-like queries over streaming data from IoT devices, applications, and social media sources.
  • Explain how Azure Event Hubs provides a big data streaming platform for ingesting millions of events per second from distributed sources with configurable retention and partitioning.
  • Explain how Azure Stream Analytics integrates with Azure Event Hubs, IoT Hub, and Blob Storage to build end-to-end streaming data pipelines with windowed aggregations and temporal queries.
  • Analyze a real-time data scenario to select the appropriate Azure streaming services by evaluating ingestion volume, query complexity, latency requirements, and downstream integration needs.

Describe data visualization with Power BI

  • Explain how Power BI Desktop, Power BI Service, and Power BI Mobile each support authoring, sharing, and consuming interactive data visualizations across different usage scenarios.
  • Explain Power BI building blocks including datasets, reports, dashboards, and workspaces and demonstrate how they connect data sources to visual analytics for business users.
  • Evaluate Power BI data connectivity options including import mode, DirectQuery, and live connection by comparing tradeoffs between data freshness, query performance, and dataset size limitations.
  • Explain how Power BI integrates with Azure data services including Azure Synapse Analytics, Azure SQL Database, and Azure Data Lake Storage to visualize cloud-hosted analytical data.
  • Analyze a reporting scenario to choose appropriate Power BI visualization types, data connectivity modes, and sharing strategies based on audience needs, data freshness requirements, and governance policies.

Describe end-to-end analytics architectures on Azure

  • Explain a modern data warehouse architecture on Azure by describing how data flows from source systems through ingestion, transformation, warehousing, and visualization using Azure data services.
  • Explain a real-time analytics architecture on Azure by describing how streaming data flows from ingestion through processing to visualization using Azure Event Hubs, Stream Analytics, and Power BI.
  • Analyze a business data scenario to design an end-to-end analytics solution on Azure by selecting appropriate services for data ingestion, storage, processing, and visualization across batch and streaming needs.

Hands-On Labs

15 labs ~204 min total Console Simulator

Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.

Certification Benefits

Salary Impact

$88,000
Average Salary

Related Job Roles

Database Administrator Data Analyst Business Intelligence Developer IT Professional

Industry Recognition

Microsoft Azure certifications are among the most valued in enterprise IT, with Microsoft holding the second-largest cloud market share globally and serving as the dominant platform in enterprise and hybrid cloud environments.

Scope

Included Topics

  • All domains in the Microsoft Azure Data Fundamentals (DP-900) exam guide: Domain 1 Describe core data concepts (25-30%), Domain 2 Identify considerations for relational data on Azure (20-25%), Domain 3 Describe considerations for working with non-relational data on Azure (15-20%), Domain 4 Describe an analytics workload on Azure (25-30%).
  • Foundational knowledge of data representation formats (structured, semi-structured, unstructured), relational and non-relational data characteristics, data analytics types (descriptive, diagnostic, predictive, prescriptive, cognitive), batch and streaming data processing, OLTP and OLAP workloads, normalization, indexing, and key concepts for relational databases.
  • Key Azure data services including Azure SQL Database, Azure SQL Managed Instance, SQL Server on Azure VMs, Azure Database for PostgreSQL, Azure Database for MySQL, Azure Database for MariaDB, Azure Cosmos DB, Azure Table Storage, Azure Blob Storage, Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Databricks, Microsoft Fabric, Azure Data Factory, Azure Stream Analytics, Azure Event Hubs, Azure Data Explorer, and Microsoft Power BI.
  • Data workload roles including database administrator, data engineer, and data analyst and their responsibilities within Azure data platform environments.
  • Scenario-driven service selection at the foundational level, data workload classification, basic data architecture reasoning, and end-to-end analytics pipeline understanding aligned to DP-900 objectives.

Not Covered

  • Deep implementation details for associate or specialty-level Azure data certifications such as DP-203, DP-300, or DP-600 not required by DP-900.
  • Hands-on T-SQL query authoring, stored procedure development, Azure CLI scripting, and SDK code implementation beyond conceptual understanding.
  • Current Azure service pricing, promotional discounts, and region-by-region pricing values that change over time.
  • Advanced data engineering patterns including complex ETL pipeline orchestration, custom Spark programming, and advanced machine learning model training.
  • On-premises SQL Server administration, Active Directory integration details, and Windows Server configuration not specific to Azure data services.

Official Exam Page

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