DP-900: Azure data fundamentals

Objectives and progression

Independent course with six modules, explained practice, and fictional APS and data-project cases. Internal assessment of 22 decisions in 50 minutes. Covers the four official domains with Microsoft Fabric and Azure Databricks in the current syllabus. DP-900 objectives effective 2026-07-21. Official exam: 45 minutes, score 700 on a 1000 scale, not equivalent to 70%. The inspected source does not set a fixed question count. Official Portuguese is Brazil.

Audience: IT professionals, APS teams, and managers working with databases and reporting.

Prerequisites: Basic application and cloud concepts. No prior certification required.

260 estimated study minutes

• Relate representation, use, and ownership before selecting storage.
• Explain what each row represents and how relationships preserve meaning.
• Choose the management level around dependencies and responsibilities.
• Match objects, files, entities, and documents to application patterns.
• Distinguish ingestion, transformation, and analysis around required latency.
• Build coherent metrics and communicate the period represented by data.

Modules

1. Data, workloads, and responsibilities
2. Relational modeling, integrity, and SQL
3. Relational services and compatibility
4. Non-relational storage and access
5. Pipelines, lakehouse, and event analytics
6. Models, freshness, and visualization

Continue learning

Data / AI

• AZ-900 — Azure Fundamentals
• SQL
• Microsoft Azure Database Administrator

References and version

DP-900 skills measured 2026-07-21

• DP-900 current skills measured 2026-07-21 · 2026-09-30
• DP-900 exam duration and languages · 2026-09-30
• Microsoft technical exam scaled scores · 2026-09-30
• Structured semi-structured and unstructured data · 2026-09-30
• CSV JSON XML Parquet Avro and Delta · 2026-09-30
• Transactional workloads and ACID · 2026-09-30
• Analytical workloads and stores · 2026-09-30
• Data store models and access patterns · 2026-09-30
• Selecting data storage · 2026-09-30
• Data engineer role and responsibilities · 2026-09-30
• Data analyst role and responsibilities · 2026-09-30
• Relational normalization principles · 2026-09-30
• Primary and foreign key constraints · 2026-09-30
• SQL statements and categories · 2026-09-30
• Views and underlying data · 2026-09-30
• Indexes and database access · 2026-09-30
• Azure SQL Database Managed Instance and virtual machines · 2026-09-30
• Managed PostgreSQL service · 2026-09-30
• Managed MySQL service · 2026-09-30
• Blob object storage · 2026-09-30
• Azure file shares and protocols · 2026-09-30
• Azure Table storage · 2026-09-30
• Cosmos DB capabilities and API families · 2026-09-30
• Cosmos DB partition keys and access patterns · 2026-09-30
• Hierarchical namespaces for data lakes · 2026-09-30
• ETL versus ELT · 2026-09-30
• Fabric SaaS analytics and OneLake · 2026-09-30
• Azure Databricks analytics platform · 2026-09-30
• Stream processing and windows · 2026-09-30
• Fabric real-time event analytics · 2026-09-30
• Fact dimension grain and relationships · 2026-09-30
• Import DirectQuery and composite models · 2026-09-30
• Visual selection by analytical purpose · 2026-09-30
• Data role boundaries and collaboration · 2026-09-30