PL-300

PL-300 Study Guide 2026: Power BI Data Analyst Exam Objectives

Updated June 202618 min read

Everything you need to pass the Microsoft Power BI Data Analyst exam — all four objective areas explained, a 4-week study plan, the Power Query and DAX topics that decide pass/fail, and what actually appears on exam day.

Quick Summary

  • • PL-300 is an Associate-level exam: 40–60 questions, ~100 minutes, 700/1000 passing score
  • • Covers Power Query data prep, DAX modeling, visualization, and the Power BI service
  • • Hands-on practice in Power BI Desktop matters more than reading — build a real report
  • • Exam cost: $165 USD
  • • No prerequisites, but it's a step up from fundamentals like PL-900 and DP-900

What is the PL-300 Exam?

The PL-300 Microsoft Power BI Data Analyst exam validates your ability to prepare, model, visualize, and analyze data with Power BI, then manage and share those assets in the Power BI service. Passing it earns the Microsoft Certified: Power BI Data Analyst Associate credential.

Unlike fundamentals exams, PL-300 tests applied skills: you connect to and clean data in Power Query, build a data model with relationships and DAX measures, design effective visuals, and configure refresh, workspaces, and row-level security. Expect scenario questions that show a model or a report requirement and ask what to do.

PL-300 is ideal for:

  • • Business and data analysts who build reports in Power BI
  • • Excel power users moving into Power BI
  • • BI developers who want a recognized Microsoft credential
  • • Anyone who finished PL-900 or DP-900 and wants the next role-based cert
DetailInformation
Exam CodePL-300
CredentialPower BI Data Analyst Associate
Questions40–60
Time Limit~100 minutes
Passing Score700 out of 1000
Price$165 USD
LevelAssociate
PrerequisitesNone (Power BI experience recommended)
RenewalAnnual free online assessment

PL-300 Exam Objectives & Weightings

The PL-300 exam covers four objective areas. The first three are weighted almost equally and together account for about 80% of your score:

Objective 1: Prepare the data

25–30%
  • Connect to data sources and choose import vs DirectQuery
  • Clean and transform data in Power Query Editor
  • Profile data: column quality, distribution, and statistics
  • Shape data: split/merge columns, pivot/unpivot, append and merge queries
  • Reduce model size and resolve data import errors

Objective 2: Model the data

25–30%
  • Design a star schema: fact and dimension tables
  • Create and configure relationships and cardinality
  • Write DAX: calculated columns vs measures
  • Use CALCULATE, SUM, RELATED, time intelligence functions
  • Row context vs filter context; create a date table and hierarchies

Objective 3: Visualize and analyze the data

25–30%
  • Choose appropriate visuals for the question
  • Format reports, bookmarks, drill-through, and tooltips
  • Configure slicers, filters, and conditional formatting
  • Add analytics: trend lines, Key influencers, decomposition tree
  • Design for accessibility and mobile layouts

Objective 4: Manage and secure Power BI

15–20%
  • Create and manage workspaces and apps
  • Configure scheduled refresh and data gateways
  • Implement row-level security (RLS) roles
  • Manage datasets, endorsement, and sharing
  • Apply sensitivity labels and workspace access roles

4-Week PL-300 Study Plan

This plan assumes 1–1.5 hours of study per day with Power BI Desktop open. PL-300 rewards hands-on practice — build a real report alongside the reading, don't just watch videos.

Week 1: Prepare the Data (Power Query)
Day 1–2Connect to sources (Excel, CSV, SQL, web). Import vs DirectQuery vs Dual storage mode and when each applies.
Day 3Power Query Editor: remove rows/columns, change data types, replace values, split and merge columns.
Day 4Pivot/unpivot, append vs merge queries, join kinds. Reference vs duplicate queries.
Day 5Data profiling: column quality, column distribution, column statistics. Spot and fix dirty data.
Day 6Reduce model size: disable load, remove unused columns, set correct data types. Handle query errors.
Day 7Practice questions: 20 on Objective 1. Review every incorrect answer.
Week 2: Model the Data (Relationships + DAX)
Day 8Star schema: fact vs dimension tables, why it matters for performance and correct totals.
Day 9Relationships: cardinality (one-to-many, many-to-many), cross-filter direction, active vs inactive.
Day 10–11DAX basics: calculated columns vs measures, SUM/SUMX, COUNTROWS, DIVIDE, RELATED, RELATEDTABLE.
Day 12CALCULATE and filter context — the single most tested DAX concept. Row context vs filter context.
Day 13Time intelligence: a proper date table, TOTALYTD, SAMEPERIODLASTYEAR, DATEADD. Hierarchies.
Day 14Practice questions: 20 on Objective 2. Focus on CALCULATE and relationship scenarios.
Week 3: Visualize, Analyze + Secure
Day 15Choose the right visual: bar/column, line, matrix, card, map, scatter. When each is appropriate.
Day 16Interactivity: slicers, filters (visual/page/report), drill-through, bookmarks, tooltips, edit interactions.
Day 17Analytics features: Key influencers, decomposition tree, Q&A, trend lines, conditional formatting.
Day 18Power BI service: workspaces, apps, scheduled refresh, on-premises data gateway.
Day 19Row-level security (RLS): roles, DAX filters, testing roles. Sensitivity labels and sharing.
Day 20Accessibility and mobile layout. Practice questions: 20 on Objectives 3 and 4.
Day 21Review all weak areas. Re-test the questions you missed during the week.
Week 4: Mock Exams + Final Review
Day 22Full mock exam (40+ questions, timed). Score and identify your two weakest objectives.
Day 23–24Targeted review of weak objectives. Rebuild any DAX or Power Query steps you got wrong.
Day 25Second mock exam. Aim for 80%+ before booking the real thing.
Day 26Drill the most-missed topics: CALCULATE, relationship direction, import vs DirectQuery, RLS.
Day 27Third mock exam under strict timing to build pacing.
Day 28Light review only. Skim your notes and the cheat-sheet topics. Rest before exam day.

The Most Tested PL-300 Topics

CALCULATE and Filter Context

The single most important DAX concept on the exam. CALCULATE evaluates an expression in a modified filter context. You must understand how it overrides or adds filters, and how filter context flows through relationships. Expect several questions that hinge on this.

Import vs DirectQuery vs Dual

Import loads data into the model (fast, full DAX, needs refresh). DirectQuery queries the source live (real-time, limited DAX/transforms, slower). Dual can act as either. The exam gives a requirement — data freshness, model size, source load — and asks which storage mode fits.

Relationship Cardinality and Cross-Filter Direction

One-to-many is the norm in a star schema. Many-to-many and bidirectional filtering solve specific problems but cause ambiguity and performance issues. Questions test whether you can pick the right relationship and explain a wrong total.

Calculated Columns vs Measures

Calculated columns are computed row by row at refresh and stored in the model (use for slicing/filtering). Measures are computed at query time in the current filter context (use for aggregations). Choosing wrong is a classic exam trap.

Row-Level Security (RLS)

Define roles with DAX filter expressions on tables, then assign users. Know static vs dynamic RLS (USERPRINCIPALNAME), how to test roles in Power BI Desktop, and that RLS is enforced in the service, not in Desktop viewing.

Power Query: Merge vs Append

Append stacks rows from queries with the same columns (union). Merge joins queries on a key, like a SQL join, adding columns. The exam describes a combine scenario and asks which to use and which join kind.

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