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PL-300
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PL300 Power BI Data Analyst

Microsoft Power BI Data Analyst (PL-300) certification teaches candidates how to prepare, model, visualize, and deploy data solutions using Power BI, enabling data-driven decision making across organizations.

100
Minutes
50
Questions
700/1000
Passing Score
$165
Exam Cost
10
Languages

Who Should Take This

Business analysts, data analysts, and reporting specialists who have at least one year of hands‑on Power BI experience and seek to validate their ability to build end‑to‑end analytics solutions should pursue this exam. They aim to deepen expertise in data preparation, modeling, visualization, and service‑level deployment to advance their careers.

What's Covered

1 Data source connections, storage modes, Power Query transformations, M language, query folding, data profiling, and dataflows.
2 Star schema design, relationships, DAX measures, calculated columns, time intelligence, semi-additive measures, and row-level security.
3 Report visuals, AI visuals, conditional formatting, slicers, drillthrough, bookmarks, themes, accessibility, and report design.
4 Workspace management, app publishing, deployment pipelines, data refresh, gateways, incremental refresh, and performance optimization.

Exam Structure

Question Types

  • Multiple Choice
  • Multiple Select
  • Case Study
  • Drag And Drop

Scoring Method

Scaled scoring from 100 to 1000 with a minimum passing score of 700

Delivery Method

Pearson VUE testing center or online proctored

Recertification

Recertify annually by passing a free online renewal assessment on Microsoft Learn.

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats

Course Outline

53 learning goals
1 Prepare the Data
3 topics

Get Data from Different Sources

  • Describe Power BI data source categories including files (Excel, CSV, JSON), databases (SQL Server, Azure SQL), online services (SharePoint, Dynamics 365), and dataflows and explain connector selection criteria
  • Implement data connections using DirectQuery, Import, and Dual storage modes and explain how each mode affects query performance, data freshness, and model size
  • Implement Power BI dataflows to create reusable, centralized data preparation logic that can be shared across multiple reports and workspaces
  • Analyze storage mode selection and evaluate trade-offs between Import, DirectQuery, and composite models for different data volume, latency, and governance requirements

Transform and Clean Data

  • Implement Power Query transformations including column renaming, type changes, fill down, unpivot, merge columns, and conditional columns to shape data for analysis
  • Implement merge queries (join types) and append queries (union) in Power Query to combine data from multiple tables and sources into unified datasets
  • Implement data profiling using column quality, column distribution, and column profile views to identify data quality issues including nulls, errors, and outliers
  • Describe query folding in Power Query and explain how it pushes transformation logic back to the source database for improved performance and reduced data transfer
  • Implement M language expressions for custom transformations including error handling with try/otherwise, custom functions, and parameter-driven queries
  • Analyze data transformation performance and evaluate optimization strategies including query folding preservation, reducing applied steps, and disabling auto date/time

Parameters and Data Source Management

  • Implement Power Query parameters to create dynamic data source connections that can be changed at deployment time for environment-specific configurations
  • Describe Power BI data source privacy levels (Public, Organizational, Private) and explain how privacy level settings affect query folding and data source combination behavior
2 Model the Data
4 topics

Design a Data Model

  • Describe star schema design principles including fact tables, dimension tables, and the benefits of denormalized dimension tables for analytical query performance in Power BI
  • Implement table relationships with appropriate cardinality (one-to-many, many-to-many) and cross-filter direction settings to enable correct measure evaluation across related tables
  • Implement role-playing dimensions using inactive relationships and USERELATIONSHIP function to support multiple date contexts (order date, ship date) from a single date dimension
  • Analyze data model design patterns and evaluate when to flatten tables versus maintain normalized relationships based on query performance, model size, and DAX complexity trade-offs

DAX Calculations

  • Describe the difference between DAX measures and calculated columns and explain how evaluation context (row context versus filter context) determines calculation behavior
  • Implement DAX measures using aggregation functions (SUM, AVERAGE, COUNT, DISTINCTCOUNT, MIN, MAX) and iterator functions (SUMX, AVERAGEX, COUNTX) for row-by-row evaluation
  • Implement DAX filter modification functions including CALCULATE, FILTER, ALL, ALLEXCEPT, REMOVEFILTERS, and KEEPFILTERS to manipulate filter context for complex business calculations
  • Implement time intelligence functions including TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, DATESYTD, and PARALLELPERIOD using a proper date dimension table for period-over-period analysis
  • Implement semi-additive measures using LASTDATE, LASTNONBLANK, and CLOSINGBALANCEMONTH for snapshot-style data where standard aggregation across time dimensions produces incorrect results
  • Analyze DAX calculation patterns and evaluate when to use measures versus calculated columns versus calculated tables and assess the performance implications of each approach

Row-Level Security

  • Implement row-level security (RLS) using DAX expressions in role definitions to restrict data access based on user identity and organizational hierarchy
  • Implement dynamic row-level security using USERPRINCIPALNAME() and security tables to manage data access through configuration rather than multiple role definitions

Advanced DAX Patterns

  • Implement DAX variables using VAR and RETURN syntax to improve calculation readability, avoid repeated expression evaluation, and simplify complex measure logic
  • Implement calculation groups using Tabular Editor to define reusable calculation patterns like time intelligence that can be applied to any base measure dynamically
  • Implement RANKX, TOPN, and SWITCH functions in DAX to create dynamic ranking measures, Top N filtering, and multi-condition formatting logic
  • Analyze DAX performance patterns and evaluate the impact of CALCULATE context transition, iterator nesting depth, and cardinality on measure evaluation speed
3 Visualize and Analyze the Data
4 topics

Report Visualizations

  • Implement standard Power BI visuals including bar/column charts, line charts, pie/donut charts, tables, matrices, cards, and KPI visuals for common reporting scenarios
  • Implement combo charts, waterfall charts, scatter plots, and ribbon charts for advanced analytical visualization scenarios requiring multiple series or category comparisons
  • Implement AI visuals including Q&A visual for natural language queries, Key Influencers for factor analysis, and Decomposition Tree for hierarchical drill-down exploration
  • Implement conditional formatting using rules, color scales, data bars, icons, and field-based formatting to highlight patterns and exceptions in report visuals

Report Interactivity

  • Implement slicers, visual-level filters, page-level filters, and report-level filters to provide interactive data exploration capabilities for report consumers
  • Implement drillthrough pages, cross-report drillthrough, and drill-down hierarchies to enable multi-level data exploration from summary to detail within and across reports
  • Implement bookmarks, buttons, and page navigation to create guided analytical experiences and toggle between different report views and visual states
  • Implement visual interactions editing to control cross-filtering and cross-highlighting behavior between visuals on a report page for precise analytical workflows

Report Design and Accessibility

  • Implement report themes, custom color palettes, and consistent formatting to create visually cohesive reports aligned with organizational branding
  • Implement report accessibility features including alt text, tab order, marker visibility, and high-contrast themes to ensure reports are usable with screen readers and assistive technologies
  • Analyze report design effectiveness and evaluate visualization choices, page layout, and interaction patterns for different audience needs ranging from executive dashboards to operational reports

Paginated Reports

  • Describe paginated reports in Power BI and explain when pixel-perfect formatted output is required for invoices, statements, and regulatory reports versus interactive Power BI reports
  • Implement basic paginated reports using Power BI Report Builder with data sources, datasets, tables, and parameters for print-ready document generation
4 Deploy and Maintain Assets
4 topics

Workspace and App Management

  • Describe Power BI Service workspace roles (Admin, Member, Contributor, Viewer) and explain how role-based permissions control content creation, modification, and consumption
  • Implement Power BI app publishing with curated navigation, audience targeting, and content organization to distribute reports and dashboards to business users
  • Implement deployment pipelines to promote content through development, test, and production stages with dataset rules and parameter updates for environment-specific configuration

Data Refresh and Gateway

  • Implement scheduled data refresh for imported datasets and configure the on-premises data gateway to enable refresh for data sources behind corporate firewalls
  • Implement incremental refresh policies using RangeStart and RangeEnd parameters to refresh only new and changed data, reducing refresh duration and resource consumption
  • Analyze refresh failure scenarios including gateway connectivity issues, credential expiration, and query timeout and evaluate diagnostic approaches using refresh history and gateway logs

Performance Optimization

  • Implement Performance Analyzer to identify slow-running visuals, diagnose DAX query duration versus visual rendering time, and export DAX queries for further optimization
  • Analyze report performance bottlenecks and evaluate optimization strategies including reducing visual count per page, optimizing DAX measures, removing unnecessary columns, and model size reduction

Sensitivity Labels and Governance

  • Implement sensitivity labels on Power BI datasets, reports, and dashboards to classify and protect content according to organizational data classification policies
  • Describe Power BI usage metrics and activity logging and explain how administrators monitor report adoption, refresh failures, and user engagement patterns
  • Implement endorsement (Promoted, Certified) for datasets and reports to help users discover trusted content and distinguish authoritative data sources from personal workspaces
  • Analyze data governance strategies for Power BI and evaluate approaches for managing self-service BI sprawl while maintaining data quality, security, and compliance across the organization

Hands-On Labs

5 labs ~80 min total Console Simulator

Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.

Certification Benefits

Salary Impact

$105,000
Average Salary

Related Job Roles

Data Analyst BI Analyst Power BI Developer Business Intelligence Developer Reporting Analyst

Industry Recognition

The PL-300 is Microsoft's associate-level certification for Power BI Data Analysts. As the most widely deployed enterprise BI platform, Power BI certification demonstrates proficiency with the tool used by over 300,000 organizations worldwide. The PL-300 is a prerequisite pathway toward the Power BI Data Analyst Expert certification.

Scope

Included Topics

  • All domains in the Microsoft PL-300 Power BI Data Analyst exam: Prepare the Data (25-30%), Model the Data (25-30%), Visualize and Analyze the Data (25-30%), and Deploy and Maintain Assets (10-15%).
  • Data acquisition using Power Query including connectors, transformations, M language basics, query folding, and data profiling.
  • Data modeling including star schema design, relationships, DAX measures, calculated columns, time intelligence, role-playing dimensions, and row-level security.
  • Visualization design including chart types, formatting, conditional formatting, bookmarks, drillthrough, tooltips, paginated reports, and AI visuals (Q&A, Key Influencers, Decomposition Tree).
  • Report and dashboard publishing to Power BI Service, workspace management, app distribution, data refresh scheduling, dataflows, and performance optimization.
  • Power BI gateway configuration, incremental refresh, deployment pipelines, and usage metrics monitoring.

Not Covered

  • Advanced DAX optimization, query plan analysis, and VertiPaq engine internals.
  • Power BI Embedded, JavaScript API, and custom visual development with D3.js.
  • Power BI Premium capacity administration, autoscale configuration, and multi-geo deployment.
  • Azure Synapse Analytics, Azure Data Factory, and broader Azure data platform integration beyond Power BI dataflows.
  • Microsoft Fabric architecture and Fabric-specific features not part of the core PL-300 exam scope.

Official Exam Page

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