PL-300 Study Guide

Microsoft Power BI Data Analyst

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Exam Quick Facts

DetailValue
Exam CodePL-300
TitleMicrosoft Power BI Data Analyst
LevelAssociate
Pass Score700 / 1000
Duration100 minutes
Questions~40–60 (multiple choice, case studies)
Cost$165 USD (varies by region)
SchedulingPearson VUE
Skills UpdatedApril 20, 2026

Official Learning Paths

  1. 📘 Prepare data for analysis with Power BI — Get data, clean data, transform data
  2. 📘 Model data with Power BI — Relationships, DAX, optimisation
  3. 📘 Visualize data with Power BI — Reports, visuals, formatting
  4. 📘 Data analysis with Power BI — AI visuals, forecasting, anomalies
  5. 📘 Manage workspaces and datasets in Power BI — Workspaces, apps, security, RLS

📖 Study Resources

ResourceLink
📝 Official Exam PageMicrosoft Learn — PL-300
📖 Official Study GuideMicrosoft Study Guide
🎯 Free Practice AssessmentStart Practice Assessment
🖥️ Exam SandboxTry the exam interface
🎬 Exam Readiness ZoneVideo prep series
📄 Power BI DocumentationPower BI docs

Skills at a Glance

Skill AreaWeight
Prepare the data25–30%
Model the data25–30%
Visualise and analyse the data25–30%
Manage and secure Power BI15–20%

Who is this exam for?

The PL-300 is for Power BI data analysts — the people who turn raw data into meaningful visualisations and insights. You connect to data sources, clean and transform data with Power Query, build data models, write DAX formulas, create reports, and manage Power BI workspaces.

This exam is very hands-on. You need to know Power Query (M language), DAX (Data Analysis Expressions), and how to design effective visualisations. The April 2026 update adds Copilot features (creating visuals with Copilot, narrative visuals) and visual calculations using DAX.

💡 Tip: The three core domains (Prepare, Model, Visualise) each carry 25–30% — they’re equally weighted. Don’t neglect any of them. DAX is the most commonly failed area, so invest extra time there.

Skills Measured — with Microsoft Learn Links

Prepare the data (25–30%)

Data preparation is the first step in any Power BI project — connecting to data sources, profiling the data to understand its quality, and transforming it into a usable shape. Power Query is the tool you’ll use for most of this work.

Get or connect to data

You can connect to hundreds of data sources — SQL databases, Excel files, SharePoint lists, web APIs, and shared semantic models. You need to choose the right connection mode: Import (loads data into Power BI), DirectQuery (queries the source live), or DirectLake (Fabric-specific, best of both).

Profile and clean the data

Before transforming data, you need to understand it. Power Query provides column profiling, data statistics, and distribution analysis. You’ll fix common issues like inconsistent values, nulls, data type mismatches, and import errors.

Transform and load the data

Transformation is where you shape data into the format your model needs — changing data types, creating calculated columns, grouping rows, pivoting/unpivoting, merging tables, and setting up proper keys for relationships.

Model the data (25–30%)

Data modelling is the heart of Power BI. A well-designed model makes reports fast and DAX formulas simple. A poorly designed model makes everything painful. Star schema (fact tables + dimension tables) is the recommended approach.

Design and implement a data model

You need to know how to configure table and column properties, create relationships with the right cardinality and cross-filter direction, implement role-playing dimensions (e.g., a single Date table used for both Order Date and Ship Date), and create a common date table.

Create model calculations by using DAX

DAX (Data Analysis Expressions) is the formula language of Power BI. You need to know how to write measures (aggregations that respond to filter context), use CALCULATE (the most important DAX function), implement time intelligence (YTD, MTD, previous year comparisons), and create calculation groups.

Optimise model performance

A slow report is a bad report. Performance Analyzer shows you which visuals are slow, DAX Query View helps identify expensive measures, and reducing table granularity (fewer rows, fewer columns) improves both speed and file size.

Visualise and analyse the data (25–30%)

This domain covers the visual layer — building reports, choosing the right chart types, using Copilot to create visuals, configuring interactivity, and performing analysis using AI features like anomaly detection and forecasting.

Create reports

Enhance reports for usability and storytelling

Good reports tell a story. Bookmarks create snapshot states, custom tooltips add context on hover, drill-through pages let users explore detail, and proper navigation makes complex reports easy to use. Accessibility is also key — ensure screen reader compatibility and keyboard navigation.

Power BI’s analytics features help you find patterns in your data. The Analyze feature explains why a value changed, grouping and binning help segment data, AI visuals (Key Influencers, Decomposition Tree) uncover drivers, and forecasting projects trends into the future.

Manage and secure Power BI (15–20%)

The smallest domain covers workspace management, content distribution (apps, subscriptions, dashboards), row-level security (RLS), sensitivity labels, and Power BI governance. This is the admin side of Power BI.

Create and manage workspaces and assets

Secure and govern Power BI items

Row-level security (RLS) restricts data access at the row level — different users see different data from the same report. Sensitivity labels from Microsoft Purview classify and protect content. You need to know how to set up both.

Skills Measured

Prepare the data (25–30%)

Get or connect to data

  • Identify and connect to data sources or a shared semantic model
  • Change data source settings, including credentials and privacy levels
  • Choose between DirectLake, DirectQuery, and Import
  • Create and modify parameters

Profile and clean the data

  • Evaluate data, including data statistics and column properties
  • Resolve inconsistencies, unexpected or null values, and data quality issues
  • Resolve data import errors

Transform and load the data

  • Select appropriate column data types
  • Create and transform columns
  • Group and aggregate rows
  • Pivot, unpivot, and transpose data
  • Convert semi-structured data to a table
  • Create fact tables and dimension tables
  • Identify when to use reference or duplicate queries and the resulting impact
  • Merge and append queries
  • Identify and create appropriate keys for relationships
  • Configure data loading for queries

Model the data (25–30%)

Design and implement a data model

  • Configure table and column properties
  • Implement role-playing dimensions
  • Define a relationship’s cardinality and cross-filter direction
  • Create a common date table
  • Identify use cases for calculated columns and calculated tables

Create model calculations by using DAX

  • Create single aggregation measures
  • Use the CALCULATE function
  • Implement time intelligence measures
  • Use basic statistical functions
  • Create semi-additive measures
  • Create a measure by using quick measures
  • Create calculated tables or columns
  • Create calculation groups

Optimize model performance

  • Improve performance by identifying and removing unnecessary rows and columns
  • Identify poorly performing measures, relationships, and visuals by using Performance Analyzer and DAX query view
  • Improve performance by reducing granularity

Visualize and analyze the data (25–30%)

Create reports

  • Select an appropriate visual
  • Format and configure visuals
  • Create a narrative visual with Copilot
  • Apply and customize a theme
  • Apply conditional formatting
  • Apply slicing and filtering
  • Use Copilot to create a new report page
  • Use Copilot to suggest content for a new report page
  • Configure the report page
  • Choose when to use a paginated report
  • Create visual calculations by using DAX

Enhance reports for usability and storytelling

  • Configure bookmarks
  • Create custom tooltips
  • Edit and configure interactions between visuals
  • Configure navigation for a report
  • Apply sorting to visuals
  • Configure sync slicers
  • Group and layer visuals by using the Selection pane
  • Configure drillthrough navigation, including pages, filters, and buttons
  • Configure export settings
  • Design reports for mobile devices
  • Enable personalization in a report, including personalized visuals
  • Design and configure Power BI reports for accessibility
  • Configure automatic page refresh
  • Use the Analyze feature in Power BI
  • Use grouping, binning, and clustering
  • Use AI visuals
  • Use reference lines, error bars, and forecasting
  • Detect outliers and anomalies
  • Use Copilot to summarize the underlying semantic model

Manage and secure Power BI (15–20%)

Create and manage workspaces and assets

  • Create and configure a workspace
  • Configure and update an app
  • Publish, import, or update items in a workspace
  • Create dashboards
  • Choose a distribution method
  • Configure subscriptions and data alerts
  • Promote or certify Power BI content
  • Identify when a gateway is required
  • Configure a semantic model scheduled refresh

Secure and govern Power BI items

  • Assign workspace roles
  • Configure item-level access
  • Configure access to semantic models
  • Implement row-level security roles
  • Configure row-level security group membership
  • Apply sensitivity labels

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