Azure DP-900: Data Fundamentals - Comprehensive Fact Sheet¶

Exam Overview¶

📖 DP-900 Exam Page - Official Microsoft certification exam page with registration details

📖 DP-900 Study Guide - Complete study guide outlining all exam objectives and skills measured

📖 Azure Data Fundamentals Learning Path - Free Microsoft Learn training path for exam preparation

The DP-900 exam validates foundational knowledge of core data concepts and Azure data services. It covers data workloads, relational and non-relational data, and analytics.

Exam Details: - Questions: 40-60 - Duration: 60 minutes - Passing Score: 700/1000 - Cost: $99 USD - Delivery: Pearson VUE (online or test center) - Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, Arabic (Saudi Arabia), Chinese (Traditional), Italian, Indonesian

Domain 1: Core Data Concepts (25-30%)¶

Data Types and Structures¶

📖 Structured vs Unstructured Data - Understanding different data types and their characteristics in Azure

📖 Data Classification Overview - Framework for classifying data in cloud environments

📖 Relational Data Concepts - Core concepts of relational databases and normalization

📖 Non-Relational Data Concepts - Key-value, document, column-family, and graph databases

Key Concepts: - Structured Data: Organized in tables with defined schema (SQL databases) - Semi-Structured Data: Has some organizational properties (JSON, XML, CSV) - Unstructured Data: No predefined structure (images, videos, binary files)

Data Workload Types¶

📖 Transactional Workloads (OLTP) - Online Transaction Processing characteristics and use cases

📖 Analytical Workloads (OLAP) - Online Analytical Processing for business intelligence

📖 Batch vs Stream Processing - Understanding different data processing patterns

📖 ETL and ELT Processes - Extract, Transform, Load data integration patterns

Workload Characteristics: - OLTP: High volume, low latency, CRUD operations, normalized schemas - OLAP: Complex queries, aggregations, read-heavy, denormalized schemas - Batch Processing: Large volumes processed at scheduled intervals - Stream Processing: Real-time data processing as it arrives

Data Roles and Responsibilities¶

📖 Database Administrator Role - Managing database availability, performance, and security

📖 Data Engineer Role - Building and maintaining data pipelines and infrastructure

📖 Data Analyst Role - Analyzing data and creating visualizations for business insights

Key Responsibilities: - Database Administrator: Backup/recovery, security, performance tuning, user access - Data Engineer: ETL pipelines, data integration, data quality, infrastructure - Data Analyst: Business intelligence, reporting, dashboards, data visualization

Domain 2: Relational Data on Azure (25-30%)¶

Azure SQL Database¶

📖 Azure SQL Database Overview - Fully managed PaaS database engine with built-in intelligence

📖 SQL Database Deployment Options - Single database, elastic pools, and managed instances

📖 SQL Database DTU-Based Model - Database Transaction Units purchasing model

📖 SQL Database vCore-Based Model - Virtual core-based purchasing with granular resource control

📖 SQL Database Service Tiers - General Purpose, Business Critical, and Hyperscale tiers

📖 SQL Database Backup and Restore - Automated backups with point-in-time restore capabilities

Key Features: - Fully managed PaaS with 99.99% SLA - Automatic patching, backups, and high availability - Built-in intelligence and threat detection - Scaling options: vertical (compute/storage) and horizontal (sharding) - Geo-replication and failover groups

Azure SQL Managed Instance¶

📖 SQL Managed Instance Overview - Near 100% SQL Server compatibility with PaaS benefits

📖 SQL Managed Instance Use Cases - Migration scenarios from on-premises SQL Server

Key Features: - Native VNET integration - SQL Server Agent support - Cross-database queries - Linked servers and CLR support - Ideal for lift-and-shift migrations

Azure SQL on Virtual Machines¶

📖 SQL Server on Azure VMs - IaaS option with full SQL Server control

📖 SQL VM Best Practices - Performance optimization guidelines for SQL VMs

Use Cases: - Need OS-level access - Custom SQL Server configurations - Legacy application compatibility - Third-party software requiring server access

Azure Database for Open Source¶

📖 Azure Database for MySQL - Fully managed MySQL database service

📖 Azure Database for PostgreSQL - Managed PostgreSQL with high availability

📖 Azure Database for MariaDB - Managed MariaDB service for cloud-native apps

📖 PostgreSQL Flexible Server - Enhanced control over database management and configuration

Relational Data Concepts¶

📖 Database Normalization - Organizing data to reduce redundancy and improve integrity

📖 SQL Query Fundamentals - SELECT, INSERT, UPDATE, DELETE operations

📖 Indexes and Performance - Optimizing query performance with proper indexing

SQL Fundamentals: - DDL: CREATE, ALTER, DROP (Data Definition Language) - DML: SELECT, INSERT, UPDATE, DELETE (Data Manipulation Language) - DCL: GRANT, REVOKE (Data Control Language) - Primary Keys: Unique identifier for each row - Foreign Keys: Establish relationships between tables - Normalization Forms: 1NF, 2NF, 3NF to reduce redundancy

Domain 3: Non-Relational Data on Azure (25-30%)¶

Azure Cosmos DB¶

📖 Azure Cosmos DB Overview - Globally distributed, multi-model NoSQL database service

📖 Cosmos DB APIs - NoSQL, MongoDB, Cassandra, Gremlin, and Table APIs

📖 Cosmos DB Consistency Levels - Strong, bounded staleness, session, consistent prefix, and eventual consistency

📖 Cosmos DB Partitioning - Horizontal partitioning for unlimited scale

📖 Cosmos DB Request Units (RU) - Understanding throughput and cost model

📖 Cosmos DB Global Distribution - Multi-region writes and automatic failover

Key Features: - Turnkey global distribution across Azure regions - Single-digit millisecond latency at 99^th percentile - Five well-defined consistency models - Multi-model support (document, key-value, graph, column-family) - Automatic indexing of all data - SLA-backed availability, throughput, latency, and consistency

Cosmos DB APIs: - NoSQL API: Native API with JSON document support - MongoDB API: MongoDB wire protocol compatibility - Cassandra API: Column-family data model - Gremlin API: Graph database with nodes and edges - Table API: Key-value pairs, Azure Table Storage upgrade path

Azure Blob Storage¶

📖 Azure Blob Storage Overview - Massively scalable object storage for unstructured data

📖 Blob Storage Access Tiers - Hot, Cool, Cold, and Archive tiers for cost optimization

📖 Blob Types - Block blobs, append blobs, and page blobs

📖 Blob Storage Lifecycle Management - Automate tier transitions and deletion

Key Features: - Store massive amounts of unstructured data - Support for files, images, videos, logs, backups - Hierarchical namespace with Azure Data Lake Storage Gen2 - Integration with analytics services - Multiple redundancy options (LRS, ZRS, GRS, GZRS)

Access Tiers: - Hot: Frequent access, highest storage cost, lowest access cost - Cool: Infrequent access (30+ days), lower storage cost - Cold: Rarely accessed (90+ days), optimized for storage - Archive: Long-term storage (180+ days), offline tier

Azure Files and Table Storage¶

📖 Azure Files Overview - Fully managed file shares using SMB and NFS protocols

📖 Azure Table Storage Overview - NoSQL key-value store for structured data

📖 Azure Queue Storage - Message queue service for asynchronous communication

Azure Files Use Cases: - Replace or supplement on-premises file servers - Lift-and-shift applications requiring file shares - Store configuration files accessible from multiple VMs - Share development tools and utilities

Table Storage Characteristics: - Schema-less design with flexible data models - Partition and row keys for data organization - Cost-effective for large volumes of structured data - No foreign keys, stored procedures, or joins

Azure Data Lake Storage Gen2¶

📖 Data Lake Storage Gen2 Overview - Blob storage with hierarchical namespace for big data analytics

📖 Data Lake Gen2 Best Practices - Optimization guidelines for analytics workloads

Key Features: - Hierarchical namespace for efficient directory operations - Hadoop-compatible access (HDFS) - Fine-grained access control with POSIX ACLs - Optimized for analytics workloads - All Blob storage features plus big data capabilities

Domain 4: Analytics Workloads on Azure (25-30%)¶

Azure Synapse Analytics¶

📖 Azure Synapse Analytics Overview - Unified analytics platform combining data warehouse and big data

📖 Synapse SQL Pools - Dedicated and serverless SQL compute options

📖 Synapse Spark Pools - Apache Spark integration for big data processing

📖 Synapse Pipelines - Data integration and ETL orchestration

📖 Synapse Studio - Unified workspace for data professionals

Key Components: - Dedicated SQL Pool: Enterprise data warehouse with MPP architecture - Serverless SQL Pool: On-demand query service, pay per query - Spark Pools: Distributed data processing with Python, Scala, .NET - Pipelines: Code-free ETL/ELT orchestration - Power BI Integration: Seamless connection to analytical datasets

Synapse Use Cases: - Enterprise data warehousing - Big data and machine learning - Real-time analytics - Data lake exploration - Unified analytics workspace

Azure Data Factory¶

📖 Azure Data Factory Overview - Cloud-based ETL and data integration service

📖 Data Factory Pipelines - Workflow orchestration and activity execution

📖 Data Factory Linked Services - Connection definitions to data stores and compute

📖 Data Factory Datasets - Data structure representations in pipelines

📖 Data Factory Integration Runtime - Compute infrastructure for data integration

📖 Data Factory Mapping Data Flows - Visual data transformation designer

Key Features: - 90+ native connectors for data sources - Code-free visual interface - Scalable, serverless data integration - Trigger-based and schedule-based execution - Monitoring and alerting capabilities - Integration with Azure DevOps and GitHub

Pipeline Components: - Activities: Units of work (copy, transform, control) - Linked Services: Connection strings to data sources - Datasets: Named views of data - Triggers: Determine pipeline execution (schedule, tumbling window, event) - Integration Runtime: Execution environment

Azure Databricks¶

📖 Azure Databricks Overview - Apache Spark-based analytics platform optimized for Azure

📖 Databricks Workspace - Collaborative environment for data engineering and science

📖 Databricks Notebooks - Interactive development with multiple language support

📖 Databricks Clusters - Compute resources for running Spark workloads

Key Features: - Optimized Apache Spark runtime - Collaborative notebooks (Python, Scala, SQL, R) - Native integration with Azure services - MLflow for machine learning lifecycle - Delta Lake for reliable data lakes - Auto-scaling and auto-termination

Use Cases: - Big data processing and ETL - Machine learning and data science - Real-time analytics and streaming - Data lake analytics - Advanced analytics and AI

Azure HDInsight¶

📖 Azure HDInsight Overview - Managed Hadoop, Spark, and Kafka clusters

📖 HDInsight Cluster Types - Apache Hadoop, Spark, HBase, Kafka, Storm, and Interactive Query

Key Features: - Open-source analytics frameworks - Enterprise-grade security with Azure AD - Cost-effective for large-scale data processing - VNET integration and encryption

Azure Stream Analytics¶

📖 Azure Stream Analytics Overview - Real-time analytics on fast-moving data streams

📖 Stream Analytics Query Language - SQL-like syntax for stream processing

📖 Stream Analytics Inputs - Event Hubs, IoT Hub, and Blob storage sources

📖 Stream Analytics Outputs - Send processed data to various Azure services

Key Features: - Real-time processing with low latency - Serverless, fully managed service - SQL-based query language - Integration with Event Hubs and IoT Hub - Built-in temporal functions (windowing) - Scalable streaming units

Common Patterns: - IoT telemetry analysis - Real-time dashboards - Anomaly detection - Log analytics and monitoring - Clickstream analysis

Power BI¶

📖 Power BI Overview - Business analytics service for interactive visualizations

📖 Power BI Components - Desktop, Service, Mobile, and Embedded

📖 Power BI Data Sources - Connect to hundreds of data sources

📖 Power BI Datasets - Data models for reports and dashboards

📖 Power BI Reports and Dashboards - Interactive visualizations and KPI monitoring

Key Components: - Power BI Desktop: Report authoring tool - Power BI Service: Cloud-based sharing and collaboration - Power BI Mobile: iOS and Android apps - Power BI Embedded: Integrate reports into applications - Power BI Report Server: On-premises reporting

Data Connectivity: - Import mode: Data cached in Power BI - DirectQuery: Real-time queries to source - Live connection: Direct connection to Azure Analysis Services - Composite models: Mix import and DirectQuery

Azure Analysis Services¶

📖 Azure Analysis Services Overview - Enterprise-grade semantic data models

📖 Analysis Services Tabular Models - In-memory and DirectQuery modes

Key Features: - High-performance semantic layer - Integration with Power BI and Excel - Row-level security - Scale-out for read-heavy workloads - Support for complex calculations (DAX)

Data Governance and Security¶

Azure Purview (Microsoft Purview)¶

📖 Microsoft Purview Overview - Unified data governance service

📖 Purview Data Catalog - Discover and understand data assets

📖 Purview Data Lineage - Track data movement and transformations

Key Features: - Automated data discovery and classification - Business glossary and data dictionary - Data lineage visualization - Sensitivity labeling - Data estate insights

Security Best Practices¶

📖 Azure SQL Security - Comprehensive security features for SQL databases

📖 Transparent Data Encryption (TDE) - Encryption at rest for databases

📖 Always Encrypted - Protect sensitive data with client-side encryption

📖 Dynamic Data Masking - Limit sensitive data exposure

📖 Row-Level Security - Control row access based on user characteristics

Security Layers: - Network Security: Firewalls, private endpoints, VNET integration - Access Management: Azure AD authentication, RBAC - Threat Protection: Advanced Threat Protection, vulnerability assessments - Information Protection: Encryption, masking, classification - Auditing: SQL auditing, diagnostic logs

Additional Resources¶

📖 Azure Pricing Calculator - Estimate costs for Azure services

📖 Azure Free Account - 12 months of free services and $200 credit

📖 Azure Documentation - Comprehensive Azure service documentation

📖 Microsoft Learn - Free, interactive learning paths and modules

📖 DP-900 Practice Assessment - Official practice questions

Study Tips and Exam Strategy¶

Preparation Approach¶

1. Complete Microsoft Learn Paths: Free, structured content aligned with exam objectives
2. Hands-On Practice: Create free Azure account and experiment with services
3. Understand Concepts: Focus on "what" and "when" rather than deep technical "how"
4. Review Documentation: Familiarize yourself with official Microsoft docs
5. Take Practice Tests: Identify knowledge gaps and build exam confidence

Key Focus Areas¶

Core Concepts to Master: - Difference between OLTP and OLAP workloads - Data types: structured, semi-structured, unstructured - Batch vs. stream processing - ETL vs. ELT patterns - CAP theorem and consistency models

Azure Services to Know: - Relational: Azure SQL Database, SQL Managed Instance, SQL on VMs, MySQL, PostgreSQL - Non-Relational: Cosmos DB (all APIs), Blob Storage, Table Storage, Data Lake Gen2 - Analytics: Synapse Analytics, Data Factory, Databricks, Stream Analytics, Power BI

Important Distinctions: - When to use SQL Database vs. Managed Instance vs. SQL on VMs - Cosmos DB API selection based on use case - Blob storage access tiers and when to use each - Synapse dedicated vs. serverless SQL pools - Data Factory vs. Synapse Pipelines

Exam Day Tips¶

• Read questions carefully, paying attention to keywords like "minimize cost," "real-time," "lowest latency"
• Eliminate obviously incorrect answers first
• For scenario-based questions, identify requirements and constraints
• Don't overthink - this is a fundamentals exam, not advanced certification
• Manage time: ~1.5 minutes per question
• Review flagged questions if time permits

Common Question Types¶

1. Service Selection: Choose appropriate Azure service for given scenario
2. Feature Identification: Match features to services
3. Best Practices: Select optimal approach for requirements
4. Troubleshooting: Identify issues and solutions
5. Terminology: Define concepts and understand relationships

Quick Reference Tables¶

Azure SQL Deployment Options¶

Cosmos DB API Selection¶

Blob Storage Access Tiers¶

Analytics Services Comparison¶

Exam Skills Measured (Current Version)¶

Describe core data concepts (25-30%)¶

• Data representation formats
• Data storage types
• Data workload types (OLTP vs OLAP)
• Batch and streaming data
• Data roles and services

Describe relational data workloads (25-30%)¶

• Relational data characteristics
• Relational database concepts (normalization, keys)
• SQL statement types (DDL, DML, DCL)
• Azure SQL Database, Managed Instance, SQL on VMs
• Azure Database for PostgreSQL, MySQL, MariaDB

Describe non-relational data workloads (25-30%)¶

• Non-relational data types (key-value, document, graph, column-family)
• Azure Cosmos DB capabilities and APIs
• Azure Blob Storage features and tiers
• Azure Files and Table Storage
• Azure Data Lake Storage Gen2

Describe analytics workloads (25-30%)¶

• Data warehouse concepts
• Data ingestion and processing
• Azure Synapse Analytics components
• Azure Data Factory pipelines
• Azure Databricks and HDInsight
• Azure Stream Analytics
• Power BI components and workflow

Glossary of Key Terms¶

ACID: Atomicity, Consistency, Isolation, Durability - properties of reliable database transactions

BASE: Basically Available, Soft state, Eventual consistency - alternative to ACID for NoSQL

CAP Theorem: Trade-off between Consistency, Availability, and Partition tolerance

Data Lake: Repository storing massive amounts of raw data in native format

Data Warehouse: Centralized repository optimized for analytics and reporting

ETL: Extract, Transform, Load - traditional data integration pattern

ELT: Extract, Load, Transform - modern pattern leveraging cloud compute

MPP: Massively Parallel Processing - distribute queries across compute nodes

Partition Key: Determines data distribution for horizontal scaling

RU: Request Unit - Cosmos DB throughput measure combining CPU, memory, IOPS

Schema-on-Read: Define structure when reading data (data lakes)

Schema-on-Write: Define structure when writing data (databases)

SLA: Service Level Agreement - guaranteed uptime and performance

Throughput: Amount of data processed in given time period

This fact sheet covers foundational concepts for the Azure DP-900 exam. Always verify information with official Microsoft documentation as Azure services evolve frequently.

Last Updated: October 2025