Microsoft Power BI Data Analyst Study Guide (2026)

Microsoft Power BI Data Analyst Study Guide 2026 – Pass on Your First Attempt

This Microsoft Power BI Data Analyst study guide covers all exam domains, key concepts, and real exam-style scenarios to help you pass on your first attempt. Learn what topics matter most, avoid common mistakes, and follow a structured plan based on the official exam blueprint.

Edureify AI helps you identify your strengths and weak areas using real exam-style questions, detailed explanations, and domain-level analysis. Get a personalized study plan, track your progress, and focus only on what will improve your Microsoft Power BI Data Analyst exam score.

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What should you study for the Microsoft Power BI Data Analyst exam?

To pass the Microsoft Power BI Data Analyst certification exam, you should focus on:

  • Prepare the Data: Covers connecting to data sources, profiling data, cleaning and transforming data using Power Query.
  • Model the Data: Covers designing and building data models including relationships, DAX calculations, and performance optimisation.
  • Visualise and Analyse the Data: Covers creating reports and dashboards, applying filters, and performing data analysis in Power BI Desktop and Service.
  • Deploy and Maintain Assets: Covers publishing to Power BI Service, managing workspaces, configuring gateways, and setting up refresh and security.

The exam tests your ability to apply concepts in real scenarios, not just memorize definitions.

Microsoft Power BI Data Analyst Exam Syllabus and Topics

The Microsoft Power BI Data Analyst exam is divided into 4 domains. Each domain tests specific skills and contributes to your overall score.

Prepare the Data

Covers connecting to data sources, profiling data, cleaning and transforming data using Power Query.

25%
Weight
13
Questions
250
Marks

Connecting to Data Sources

  • Connecting to files: Excel, CSV, JSON, XML, and PDF
  • Connecting to databases: SQL Server, Azure SQL, PostgreSQL, Oracle
  • Connecting to cloud services: SharePoint, Dynamics 365, Salesforce
  • Import mode vs DirectQuery vs Dual mode trade-offs
  • Using parameters and data source credentials

Data Profiling and Quality

  • Column distribution, column quality, and column profile views
  • Identifying nulls, duplicates, and outliers
  • Data types in Power Query and type errors
  • Enabling load vs disabling load for staging queries

Power Query Transformations

  • Filtering, sorting, and removing rows and columns
  • Pivoting and unpivoting columns for normalisation
  • Merging queries: join kinds (inner, left outer, full outer, anti)
  • Appending queries to combine tables
  • Using custom columns, conditional columns, and M expressions
  • Handling errors and replacing values

Advanced Query Techniques

  • Query folding: what it is and how to verify it occurs
  • Grouping and aggregating data in Power Query
  • Extracting data from nested JSON and XML
  • Using reference queries vs duplicate queries

Model the Data

Covers designing and building data models including relationships, DAX calculations, and performance optimisation.

25%
Weight
13
Questions
250
Marks

Data Model Design

  • Star schema vs snowflake schema design principles
  • Fact tables and dimension tables: roles and best practices
  • Creating and managing table relationships in Model view
  • Cardinality: one-to-many, many-to-many, one-to-one
  • Cross-filter direction: single vs both and performance impact
  • Role-playing dimensions and inactive relationships

Calculated Tables and Columns

  • Calculated columns vs measures: when to use each
  • Creating date tables with CALENDAR() and CALENDARAUTO()
  • Marking a date table for time intelligence
  • Calculated tables using FILTER, ALL, and SUMMARIZE

DAX Fundamentals

  • DAX syntax, operators, and data types
  • CALCULATE() function: modifying filter context
  • FILTER(), ALL(), ALLEXCEPT(), and REMOVEFILTERS()
  • Iterator functions: SUMX, AVERAGEX, RANKX
  • Time intelligence functions: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD
  • RELATED() and RELATEDTABLE() for navigating relationships

Advanced DAX Patterns

  • Row context vs filter context and context transition
  • Variables in DAX: VAR and RETURN
  • Measures for percentage of total and running totals
  • Dynamic segmentation with SWITCH() and IF()

Performance Best Practices

  • Reducing model size: removing unused columns and tables
  • Aggregations and pre-aggregation tables
  • Using Performance Analyser to identify slow visuals
  • DAX Studio and Tabular Editor for optimisation
  • Optimising DirectQuery models

Visualise and Analyse the Data

Covers creating reports and dashboards, applying filters, and performing data analysis in Power BI Desktop and Service.

25%
Weight
13
Questions
250
Marks

Visualisation Types and Selection

  • Choosing appropriate visuals: bar, line, scatter, map, pie, treemap
  • Card and multi-row card for KPI display
  • Matrix and table visuals: conditional formatting and totals
  • Waterfall, funnel, and decomposition tree visuals
  • Custom visuals from AppSource

Interactivity and Filtering

  • Slicers: list, dropdown, date range, and relative date
  • Cross-filtering and cross-highlighting between visuals
  • Edit interactions to control visual behaviour
  • Drillthrough pages and report tooltips
  • Buttons, bookmarks, and page navigation

Report Design

  • Themes, templates, and consistent colour palettes
  • Mobile layout and responsive design
  • Accessibility: alt text, tab order, and keyboard navigation
  • Page navigation with buttons and selection pane

Advanced Analytics

  • Analytics pane: trend lines, forecast, and constant lines
  • Q&A visual and natural language queries
  • Key influencers and decomposition tree for explanatory analysis
  • Anomaly detection in line charts
  • Smart narratives for automated text insights

Deploy and Maintain Assets

Covers publishing to Power BI Service, managing workspaces, configuring gateways, and setting up refresh and security.

25%
Weight
13
Questions
250
Marks

Publishing and Workspaces

  • Publishing reports from Desktop to Power BI Service
  • Workspace roles: Admin, Member, Contributor, Viewer
  • Apps: creating, publishing, and updating workspace apps
  • Deployment pipelines: Development, Test, Production stages

Data Refresh and Gateways

  • Scheduled refresh: configuring and troubleshooting
  • On-premises data gateway: standard vs personal mode
  • Incremental refresh policies using RangeStart and RangeEnd
  • Dataflows and their refresh dependencies

Security and Governance

  • Row-level security (RLS): static and dynamic roles
  • Object-level security (OLS) for column and table restrictions
  • Sensitivity labels and Microsoft Purview integration
  • Endorsement: promoting and certifying datasets
  • Lineage view and impact analysis in Power BI Service

Sharing and Collaboration

  • Sharing reports vs sharing dashboards vs publishing apps
  • Embedding reports in SharePoint and Teams
  • Subscriptions and data alerts
  • Usage metrics and monitoring report performance
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Learning Progress

Prepare the Data 85%
Model the Data 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

Frequently Asked Questions