Microsoft Azure Data Fundamentals (DP-900): 5 Must-Know Concepts

The Azure Data Fundamentals exam (DP-900) validates foundational knowledge of core data concepts and how they map to Microsoft Azure data services. These five concepts are the spine of the exam outline.

1. Core Data Concepts#

Core data concepts make up roughly 25–30% of DP-900. Service questions get easier once this vocabulary is automatic.

Types of Data#

Batch vs Real-Time Processing#

Data Roles#

Test Your Understanding#

2. Relational Databases on Azure#

Relational database concepts make up 20-25% of the exam. Understanding how tables relate and Azure's SQL offerings is crucial.

Relational Database Fundamentals#

Relational databases organize data into tables (also called relations) with:

• Rows — Individual records (e.g., one customer, one order)
• Columns — Attributes of the data (e.g., name, email, date)
• Primary Key — Unique identifier for each row
• Foreign Key — Column referencing another table's primary key

┌─────────────────────────────┐     ┌─────────────────────────────┐
│        Customers            │     │          Orders             │
├─────────────────────────────┤     ├─────────────────────────────┤
│ CustomerID (PK)             │◄────│ CustomerID (FK)             │
│ Name                        │     │ OrderID (PK)                │
│ Email                       │     │ OrderDate                   │
└─────────────────────────────┘     │ TotalAmount                 │
                                    └─────────────────────────────┘

Azure Relational Data Services#

Key SQL Concepts#

3. Non-Relational Data on Azure#

Non-relational (NoSQL) data services make up 15-20% of the exam. These services handle data that doesn't fit neatly into tables.

Azure Cosmos DB#

Azure Cosmos DB is Microsoft's globally distributed, multi-model NoSQL database. Key features:

• Global distribution — Replicate data to any Azure region
• Multi-model — Support multiple APIs for different data models
• Guaranteed low latency — Single-digit millisecond reads and writes
• Automatic scaling — Throughput scales with demand

Cosmos DB APIs#

Partition Keys#

Cosmos DB uses partition keys to distribute data across physical partitions:

• Choose a property with high cardinality (many unique values)
• Queries should filter by partition key when possible
• Avoid "hot partitions" where one partition gets most traffic

Azure Blob Storage#

Azure Blob Storage stores unstructured data like images, videos, and backups:

Azure Data Lake Storage Gen2#

Combines blob storage with a hierarchical namespace for big data analytics:

• Hierarchical namespace — True directory structure for better performance
• Hadoop compatible — Works with Azure Databricks, HDInsight, Synapse
• Cost effective — Blob storage pricing with data lake capabilities

4. Data Analytics on Azure#

Analytics concepts make up 25-30% of the exam. Understanding data warehousing and Azure's analytics services is essential.

Data Warehouse vs Data Lake#

Azure Synapse Analytics#

Azure Synapse is an integrated analytics service that brings together:

• Dedicated SQL pools — Data warehousing with massively parallel processing
• Serverless SQL pools — Query data lake files with T-SQL (pay per query)
• Apache Spark pools — Big data processing and machine learning
• Pipelines — Data integration and orchestration (like Data Factory)

Azure Data Factory#

Azure Data Factory (ADF) is a cloud ETL service for data integration:

• Pipelines — Orchestrate data movement and transformation
• Activities — Individual operations (copy data, run scripts)
• Linked Services — Connections to data sources and destinations
• Datasets — Representations of data structures

Azure Databricks#

A collaborative Apache Spark platform for big data and machine learning:

• Notebooks — Interactive code development
• Clusters — Managed Spark compute
• Delta Lake — Reliable data lake with ACID transactions
• MLflow — Machine learning lifecycle management

5. Data Visualization with Power BI#

Understanding how data flows from source to dashboard is critical for the exam.

Power BI Components#

Data Sources → Dataset → Report(s) → Dashboard
     │              │          │           │
  SQL, Excel    Data Model   Charts    Pinned Tiles

Power BI Workflow#

1. Connect — Link to data sources (databases, files, services)
2. Transform — Clean and shape data with Power Query
3. Model — Define relationships, measures, calculated columns
4. Visualize — Create charts, tables, and interactive reports
5. Share — Publish to Power BI Service, create dashboards

Key Power BI Concepts#

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Know your range before test day#

DP-900 is concept-heavy. Use a short diagnostic to see which data domains need drills before you book the sitting.

• Timed diagnostic with domain-level breakdown
• Clear signal on what to drill next
• Practice free; timed sittings and readiness range on Pro

Take the free DP-900 assessment →

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Next step: Start practicing free on the DP-900 hub, or sit a short diagnostic first.