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Guided DP-900 Domain 4
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DP-900 Study Guide

Domain 1: Core Data Concepts

  • Your First Look at Data Free
  • Data File Formats: CSV, JSON, Parquet & More Free
  • Databases: Relational vs Non-Relational Free
  • Transactional Workloads: Keeping Data Consistent Free
  • Analytical Workloads: Finding the Insights Free
  • Data Roles: DBA, Engineer & Analyst Free
  • The Azure Data Landscape Free

Domain 2: Relational Data on Azure

  • Relational Data: Tables, Keys & Relationships
  • Normalization: Why Duplicate Data is Bad
  • SQL Basics: SELECT, INSERT, UPDATE, DELETE
  • Database Objects: Views, Indexes & More
  • Azure SQL: Your Database in the Cloud
  • Open-Source Databases on Azure
  • Choosing the Right Azure Database

Domain 3: Non-Relational Data on Azure

  • Azure Blob Storage: Files in the Cloud
  • Azure Files & Table Storage
  • Azure Cosmos DB: The Global Database
  • Cosmos DB APIs: SQL, MongoDB & More
  • Choosing Non-Relational Storage

Domain 4: Analytics on Azure

  • Data Ingestion & Processing
  • Analytical Data Stores: Data Lakes, Warehouses & Lakehouses
  • Microsoft Fabric & Azure Databricks
  • Batch vs Streaming: Two Speeds of Data
  • Real-Time Analytics on Azure
  • Power BI: See Your Data
  • Data Models in Power BI
  • Choosing the Right Visualization

DP-900 Study Guide

Domain 1: Core Data Concepts

  • Your First Look at Data Free
  • Data File Formats: CSV, JSON, Parquet & More Free
  • Databases: Relational vs Non-Relational Free
  • Transactional Workloads: Keeping Data Consistent Free
  • Analytical Workloads: Finding the Insights Free
  • Data Roles: DBA, Engineer & Analyst Free
  • The Azure Data Landscape Free

Domain 2: Relational Data on Azure

  • Relational Data: Tables, Keys & Relationships
  • Normalization: Why Duplicate Data is Bad
  • SQL Basics: SELECT, INSERT, UPDATE, DELETE
  • Database Objects: Views, Indexes & More
  • Azure SQL: Your Database in the Cloud
  • Open-Source Databases on Azure
  • Choosing the Right Azure Database

Domain 3: Non-Relational Data on Azure

  • Azure Blob Storage: Files in the Cloud
  • Azure Files & Table Storage
  • Azure Cosmos DB: The Global Database
  • Cosmos DB APIs: SQL, MongoDB & More
  • Choosing Non-Relational Storage

Domain 4: Analytics on Azure

  • Data Ingestion & Processing
  • Analytical Data Stores: Data Lakes, Warehouses & Lakehouses
  • Microsoft Fabric & Azure Databricks
  • Batch vs Streaming: Two Speeds of Data
  • Real-Time Analytics on Azure
  • Power BI: See Your Data
  • Data Models in Power BI
  • Choosing the Right Visualization
Domain 4: Analytics on Azure Premium ⏱ ~12 min read

Data Models in Power BI

Great dashboards start with a great data model. Star schemas, relationships, measures, and dimensions — the foundation of Power BI reporting.

What is a data model?

☕ Simple explanation

A data model is the blueprint telling Power BI how tables connect.

Three spreadsheets: Sales, Products, Stores. The model maps “ProductID in Sales matches ProductID in Products.” Without this map, Power BI can’t combine data.

Good model = fast, accurate reports. Bad model = wrong numbers.

A data model defines tables, columns, relationships, measures, hierarchies, and data types. Power BI uses it to combine tables, aggregate values, and filter results. The recommended pattern is the star schema.

Star schema

The most common pattern: a central fact table surrounded by dimension tables.

  • Fact table (centre): Measurable events — numeric values and foreign keys
  • Dimension tables (points): Descriptive attributes for filtering

Priya’s FreshMart model:

  • Sales (fact): DateKey, StoreKey, ProductKey, Quantity, Revenue, Cost
  • Date (dim): DateKey, Month, Quarter, Year
  • Product (dim): ProductKey, Name, Category, Brand
  • Store (dim): StoreKey, StoreName, City, Region
Fact vs dimension tables
FeatureFact TablesDimension Tables
ContainsMeasurable eventsDescriptive attributes
Row countVery large (millions+)Small (thousands)
Key columnsForeign keys + measuresPrimary key + descriptions
ExamplesSales, Orders, DeliveriesDate, Product, Store

Measures vs calculated columns

MeasuresCalculated Columns
WhenQuery time (dynamic)Data refresh (stored)
ExampleTotal Revenue = SUM(Sales[Revenue])Profit = Revenue - Cost
Best forAggregations with filtersRow-level calculations

Hierarchies

Drill-down paths: Year → Quarter → Month → Day. Users click to drill from yearly to monthly.

💡 Exam tip: data model concepts
  • “Central table with numeric measures” → Fact table
  • “Descriptive attributes for filtering” → Dimension table
  • “Year → Quarter → Month” → Hierarchy
  • “SUM that changes with filters” → Measure
  • “Recommended schema” → Star schema

Flashcards

Question

What is a star schema?

Click or press Enter to reveal answer

Answer

A data model with a central fact table (events + measures) surrounded by dimension tables (descriptive attributes). Foreign keys connect them — forming a star shape.

Click to flip back

Question

Fact table vs dimension table?

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Answer

Fact: measurable events (revenue, quantity), very large, grows constantly. Dimension: descriptive attributes (product name, date), relatively small, changes rarely.

Click to flip back

Question

What is a measure in Power BI?

Click or press Enter to reveal answer

Answer

A DAX formula calculated at query time that changes with filter context. Example: Total Revenue = SUM(Sales[Revenue]). Computed on demand, not stored.

Click to flip back

Knowledge check

Knowledge Check

Priya's Sales table has DateKey, StoreKey, ProductKey, Quantity, Revenue. What type?

Knowledge Check

Total revenue changes when a user selects a region filter. This uses:

🎬 Video coming soon

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