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PL-300: Power BI Data Analyst
Microsoft Certified: Data Analyst Associate
PL-300 ⏱ 100 min ✅ 700/1000 40–60 Qs 💲 USD 165
Study Guide · MCT Recommended Path
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Read the official Microsoft study guide
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Build real solutions with hands-on labs
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Take Microsoft's free official assessment
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Practice here until you score 80%+
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PL-300 Power BI Data Analyst — Complete Exam Reference

What is the PL-300 Exam?

The Microsoft PL-300 (Power BI Data Analyst Associate) exam validates skills in preparing data using Power Query, modeling data with DAX, creating reports and dashboards, analyzing data with AI visuals, and deploying solutions to Power BI Service. Passing earns the Microsoft Certified: Data Analyst Associate credential.

PL-300 Exam Details

  • Duration: 100 minutes
  • Questions: 40–60 questions
  • Passing score: 700 out of 1000
  • Cost: USD 165 (approximately ₹13,800)
  • Validity: Annual renewal required
  • Delivery: Pearson VUE — online or test centre

PL-300 Exam Domains

  • Prepare Data — 25–30% — Power Query, data sources, profiling
  • Model Data — 25–30% — Relationships, DAX, RLS
  • Visualize & Analyze — 25–30% — Reports, dashboards, AI visuals
  • Deploy & Maintain — 15–20% — Workspaces, pipelines, refresh

Key DAX Functions Tested in PL-300

  • CALCULATE() — context modification, the most important DAX function
  • SUMX() / AVERAGEX() — iterator functions for row-level calculations
  • RANKX() — dynamic rankings with ALL() and ALLEXCEPT()
  • TOTALYTD() / SAMEPERIODLASTYEAR() — time intelligence
  • DIVIDE() — safe division returning BLANK on zero denominator
  • FILTER() — table filtering inside CALCULATE

Common PL-300 Exam Mistakes

  • Confusing calculated columns (row context) with measures (filter context)
  • Not marking the Date table — breaks all time intelligence functions
  • Using bidirectional relationships unnecessarily — causes ambiguous filter paths
  • Forgetting that RANKX with ALL() ignores visual filters
  • Applying RLS in Power BI Desktop instead of Power BI Service (wrong order)
  • Choosing DirectQuery when Import mode is more appropriate

When to Use Import vs DirectQuery

  • Import Mode: Dataset fits in memory, daily refreshes acceptable, need full DAX support
  • DirectQuery: Data changes frequently, dataset too large, near-real-time required
  • Composite Model: Combine Import tables with DirectQuery for flexibility
  • Direct Lake (Fabric): OneLake data with Import-like performance — no data copy needed

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