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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 ⏱ ~10 min read

Choosing the Right Visualization

The wrong chart misleads. The right chart tells a story instantly. Learn which Power BI visual suits each type of data.

Why visualisation choice matters

☕ Simple explanation

The right chart is like the right map for a journey.

Show change over time? Line chart. Compare categories? Bar chart. Single number? Card. Picking right means your audience gets the point instantly.

Data visualisation maps data to visual properties (position, length, colour) to communicate patterns. The DP-900 exam tests matching data types and questions to chart types.

Visualisation guide

Comparison visuals

VisualBest ForExample
Bar chartComparing categoriesRevenue by product
Column chartComparing groupsSales by store
Clustered barSub-groups within categoriesSales by store, split by type

Trend visuals

VisualBest ForExample
Line chartChange over timeMonthly revenue over 2 years
Area chartTrends with volumeCumulative sales

Part-of-whole visuals

VisualBest ForExample
Pie chartSimple proportion (max 5-6 slices)Market share
TreemapHierarchical breakdownSales by category

Other visuals

VisualBest ForExample
Scatter plotRelationship between two measuresPrice vs demand
MapGeographic dataSales by city
CardSingle KPITotal revenue: $2.3M
KPITarget vs actualRevenue vs target
Table/MatrixDetailed drill-downStore-by-store monthly data
Match questions to visuals
QuestionBest Visual
How does X compare to Y?Bar or column chart
How has X changed over time?Line chart
What proportion is X?Pie or treemap
Relationship between X and Y?Scatter plot
Where is X happening?Map
What is the current value?Card or KPI
ℹ️ Common mistakes
  • Pie charts with 6+ slices — use bar charts instead
  • 3D charts — distort data. Always 2D
  • Line charts for categories — lines imply continuous trends
💡 Exam tip: visual matching
  • “Sales trend over months” → Line chart
  • “Compare 5 stores” → Bar chart
  • “Market share” → Pie chart
  • “Price vs demand” → Scatter plot
  • “Sales by city” → Map
  • “Single KPI number” → Card

Flashcards

Question

When to use a line chart?

Click or press Enter to reveal answer

Answer

Show trends over time — how values change across days, months, years.

Click to flip back

Question

Bar chart vs pie chart?

Click or press Enter to reveal answer

Answer

Bar charts handle many categories clearly. Pie charts work for 5-6 slices max. Use bar for 6+.

Click to flip back

Question

What is a scatter plot for?

Click or press Enter to reveal answer

Answer

Showing relationships between two numeric measures. Each point positioned by X and Y values.

Click to flip back

Knowledge check

Knowledge Check

Priya wants to show monthly revenue over 2 years. Which visual?

Knowledge Check

Tom needs to compare completed vs late deliveries across 5 depots. Which visual?

Knowledge Check

The CEO wants one number: this month's total revenue. Which visual?

🎬 Video coming soon

You’ve completed the entire DP-900 course! 🎉

You’ve covered core data concepts, relational and non-relational databases on Azure, and the full analytics stack from ingestion to Power BI.

What’s next?

  • Review tricky modules
  • Practice questions (coming soon)
  • Book your exam at Microsoft Learn

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