DP-900 Azure Data Fundamentals Practice Exam Questions 2026
Prepare for the DP-900 exam with confidence! This set includes 230 unique practice questions created from scratch and fully compliant with the official 2026 exam syllabus. The DP-900: Microsoft Azure Data Fundamentals exam syllabus is structured around four main domains, covering core data concepts and how they are implemented using Microsoft Azure data services. Domain Approximate Weighting 1. Describe core data concepts 25−30% 2. Identify considerations for relational data on Azure 20−25% 3. Describe considerations for working with non-relational data on Azure 15−20% 4. Describe an analytics workload on Azure 25−30% 1. Describe Core Data Concepts (25−30%) Describe ways to represent data: Features of structured , semi-structured , and unstructured data. Identify options for data storage: Common formats for data files. Types of databases (e.g., relational, non-relational). Describe common data workloads: Features of transactional (OLTP) workloads. Features of analytical (OLAP) workloads. Identify roles and responsibilities for data workloads: Responsibilities for Database Administrators, Data Engineers, and Data Analysts. 2. Identify Considerations for Relational Data on Azure (20−25%) Describe relational concepts: Features of relational data (tables, columns, rows). Normalization and why it is used. Common SQL statements (DDL and DML). Common database objects (tables, views, stored procedures). Describe relational Azure data services: The Azure SQL family of products (Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines). Azure database services for open-source database systems (e.g., Azure Database for PostgreSQL, Azure Database for MySQL). 3. Describe Considerations for Working with Non-Relational Data on Azure (15−20%) Describe capabilities of Azure storage: Azure Blob storage . Azure File storage . Azure Table storage . Describe capabilities and features of Azure Cosmos DB: Identify use cases for Azure Cosmos DB (globally distributed, multi-model). Describe Azure Cosmos DB APIs (e.g., SQL, MongoDB, Cassandra). 4. Describe an Analytics Workload on Azure (25−30%) Describe common elements of large-scale analytics: Considerations for data ingestion and processing (ETL/ELT). Options for analytical data stores (e.g., Data Lakes, Data Warehouses). Microsoft cloud services for large-scale analytics, including Azure Synapse Analytics and Azure Databricks . Describe consideration for real-time data analytics: Difference between batch and streaming data. Technologies for real-time analytics (e.g., Azure Stream Analytics). Describe data visualization in Microsoft Power BI: Identify capabilities of Power BI (interactive reports, dashboards). Describe features of data models in Power BI. Who this course is for: The ideal starting point for students planning to move into advanced Azure data certifications. For students who prefer to build a solid conceptual base before tackling specialized Azure data exams. Future advanced Azure data certification candidates who need to master the fundamentals first.
What you'll learn
- Master Python for Artificial Intelligence: Write efficient Python code, essential for AI and ML programming tasks.
- Data Preprocessing Skills for Artificial Intelligence: Prepare, clean, and transform data to enhance model performance.
- Statistical Knowledge for Artificial Intelligence: Apply core statistics to understand data patterns and inform decisions.
- Build Machine Learning Models for Artificial Intelligence: Develop and fine-tune ML models for classification, regression, and clustering.
- Deep Learning Proficiency: Design and train neural networks, including CNNs and RNNs, for image and sequence tasks.
- Utilize Transfer Learning: Adapt pre-trained models to new tasks, saving time and resources.
- Deploy ML Models with APIs: Create scalable APIs to serve ML models in real-world applications.
- Containerize with Docker: Package models for portable deployment across environments.
- Monitor and Maintain Models: Track model performance, detect drift, and implement retraining pipelines.
- Complete ML Lifecycle: Master end-to-end AI project skills, from data to deployment and ongoing maintenance.
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