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Microsoft Power BI Data Analyst (PL-300) Fact Sheet

Exam Overview

Exam Code: PL-300 Exam Name: Microsoft Power BI Data Analyst Level: Associate Duration: 100 minutes Format: Multiple choice, multiple select, drag-and-drop, case studies, and occasionally a lab Questions: Typically 40-60 Passing Score: 700 out of 1000 Cost: USD 165 (varies by country) Valid For: 1 year, renewable free online through Microsoft Learn Delivery: Pearson VUE, test center or online proctored Prerequisites: None formally; hands-on Power BI Desktop experience strongly expected

Verify before booking. Confirm the current outline and price on the official pages below.

📖 PL-300 certification page - registration and renewal 📖 PL-300 study guide - the authoritative skills-measured outline 📖 Power BI documentation - product reference

Why this exam is in this repo

The repo covers DP-600 and DP-700 for Fabric engineering, and DP-203 for data engineering, but had no analyst-tier certification. PL-300 is the highest-volume analytics certification in the market and the natural on-ramp to the Fabric track: the semantic modeling and DAX knowledge it builds is a direct prerequisite for DP-600.

It also fills a career-path gap. Not everyone entering data work starts as an engineer; many start as an analyst.

Target Audience

  • Data analysts building reports and semantic models
  • Business intelligence developers
  • Finance, operations, and marketing professionals who own reporting
  • Data engineers who need to understand the consumption layer
  • Anyone heading toward DP-600

Exam Domains

Domain 1: Prepare the data (25-30%)

Key Concepts: - Connectivity: files, folders, databases, SharePoint, web, dataflows, OData, and the Fabric lakehouse - Storage modes: Import, DirectQuery, Dual, and Direct Lake, and the trade-offs of each - Composite models and table-level storage mode selection - Power Query transformations: shape, filter, group, pivot and unpivot, split, merge, append - Data profiling: column quality, distribution, and profile - Handling errors, nulls, and inconsistent types - Query folding: what it is, how to check it, and why breaking it matters - Parameters and functions in Power Query - Incremental refresh configuration and its RangeStart and RangeEnd requirement - Dataflows Gen1 and Gen2 for reusable preparation - The on-premises data gateway: standard versus personal mode

📖 Power Query documentation - transformation reference 📖 Storage modes in Power BI - Import, DirectQuery, Dual

Domain 2: Model the data (25-30%)

Key Concepts: - Star schema design: fact and dimension tables, and why it beats a flat table - Relationships: cardinality, cross-filter direction, active and inactive, bidirectional filtering risks - Role-playing dimensions and USERELATIONSHIP - Date table creation and marking, and why time intelligence needs one - DAX fundamentals: calculated columns, measures, calculated tables, and when each is appropriate - Filter context and row context; CALCULATE as the context-modifying function - Iterator functions (SUMX, AVERAGEX) and when they are necessary - Time intelligence: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, DATESYTD - Variables in DAX for readability and performance - Row-level security: static and dynamic roles, USERPRINCIPALNAME, and testing with View As - Object-level security concepts - Hierarchies, display folders, and field parameters - Calculation groups - Performance: Performance Analyzer, reducing cardinality, avoiding bidirectional filters, aggregations

📖 DAX reference - function reference 📖 Star schema guidance - the modeling approach the exam assumes

Domain 3: Visualize and analyze the data (25-30%)

Key Concepts: - Visual selection: which chart answers which question - Formatting, conditional formatting, and the design pane - Slicers, sync slicers, filters at visual, page, and report level, and the filter pane - Drillthrough, drilldown, tooltips, and report page tooltips - Bookmarks, selection pane, and buttons for navigation - Custom and AppSource visuals, and organizational visual governance - Accessibility: alt text, tab order, color contrast, and report readability - Mobile layouts - AI visuals: key influencers, decomposition tree, smart narrative, Q&A, anomaly detection - Quick measures and the quick measure suggestions experience - Analyze in Excel, paginated report basics, and when a paginated report is the right tool - Identifying outliers, trends, and correlations from a report

📖 Power BI visualizations - visual types and configuration 📖 Report accessibility - accessible report design

Domain 4: Manage and secure Power BI (15-20%)

Key Concepts: - Workspaces: roles (Admin, Member, Contributor, Viewer) and what each can do - Apps: publishing, audiences, and the difference between app and workspace access - Sharing: reports, dashboards, links, and the security implications of each - Semantic model settings: scheduled refresh, gateway binding, credentials, and refresh failures - Row-level security applied through workspace and app membership - Sensitivity labels on Power BI content and their inheritance - Deployment pipelines: development, test, and production stages with rules - Endorsement: promoted and certified content - Usage metrics and audit - Capacity concepts: Pro, Premium Per User, and Fabric capacity - Managing the semantic model as a shared asset

📖 Power BI security - workspace and content security 📖 Deployment pipelines - ALM for Power BI content

Storage mode quick reference

Mode Data location Refresh Choose when
Import Cached in the model Scheduled or on demand Best performance, data volume fits, latency tolerable
DirectQuery Stays in the source Query at interaction time Near-real-time need, or volume too large to import
Dual Both, decided per query Both Dimension tables serving both Import and DirectQuery facts
Direct Lake Parquet in OneLake, read directly No import step Fabric lakehouse workloads needing import-like speed at scale