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Passing Score, Duration & Questions For Microsoft PL-300 Exam

Time Duration: 180 minutes
The passing score: 700
Number of Questions: 40-60 questions
Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300

Exam PL-300: Microsoft Power BI Data Analyst

The Power BI data analyst delivers actionable insights by leveraging available data and applying domain expertise. The Power BI data analyst collaborates with key stakeholders across verticals to identify business requirements, cleans and transforms the data, and then designs and builds data models by using Power BI. The Power BI data analyst provides meaningful business value through easy-to-comprehend data visualizations, enables others to perform self-service analytics, and deploys and configures solutions for consumption. Candidates for this exam should be proficient using Power Query and writing expressions by using DAX.

Passing score: 700. Learn more about exam scores.

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Part of the requirements for: Microsoft Certified: Power BI Data Analyst Associate

Microsoft PL-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage and Maintain Workspace Items	10-15%	- Manage datasets 1. Configure dataset metadata and properties 2. Manage gateways and data sources 3. Manage dataset refresh scheduling 4. Resolve dataset permission issues - Create and manage workspaces 1. Endorse sensitive content with sensitivity labels 2. Configure content certification 3. Configure workspace membership and roles 4. Publish and update a Power BI app
Topic 2: Prepare the Data	10-15%	- Clean, transform, and load the data 1. Resolve data quality issues 2. Apply user-friendly naming conventions to columns and tables 3. Configure data loading and queries 4. Profile the data 5. Identify the data structure 6. Design and build intermediate tables 7. Determine proper data types - Get data from various data sources 1. Choose between DirectQuery, Import, and Dual storage modes 2. Configure dataset refresh 3. Use Microsoft Fabric and OneLake 4. Select a shared dataset or create a local copy 5. Change data source settings 6. Identify and connect to a data source
Topic 3: Analyze the Data	10-15%	- Work with metrics in Power BI 1. Create metrics using Microsoft measurements 2. Define and monitor KPIs 3. Use the Decomposition Tree visual 4. Track goals with scorecards - Perform analysis in Power BI 1. Apply Quick Insights 2. Use the Q&A visual 3. Identify patterns and trends 4. Configure auto-insights 5. Generate Pinn visual insights
Topic 4: Visualize the Data	25-30%	- Build visuals 1. Configure visual interactions and cross-filtering 2. Apply AI insights and enhanced analytics 3. Format and configure pages and report backgrounds 4. Configure data colors and shapes 5. Apply sorting and filtering to visuals 6. Determine the appropriate visual type - Enhance reports for user experience 1. Add accessibility features and compliance 2. Apply artistic formatting to visuals 3. Add tooltips and navigation 4. Design for mobile and specific layouts - Enrich reports with advanced visuals 1. Create custom visuals 2. Configure R and Python visuals 3. Use dynamic page navigation 4. Integrate Microsoft Copilot for Power BI
Topic 5: Model the Data	25-30%	- Design a data model 1. Apply field metadata and groupings 2. Implement row-level security 3. Determine the appropriate granularity 4. Resolve performance issues with relationships 5. Define tables and table schemas 6. Design a star schema or snowflake schema - Create measures and DAX calculations 1. Use CALCULATE and FILTER functions 2. Use Time Intelligence functions 3. Write and use basic DAX syntax 4. Implement context transition 5. Create calculated measures 6. Create semi-additive measures - Optimize model performance 1. Validate performance and resolve issues 2. Optimize Dax queries 3. Improve user experience by hiding objects 4. Optimize DirectQuery models 5. Configure aggregations - Develop the data model 1. Build hierarchies and hierarchical fields 2. Use the calculation group feature 3. Configure table relationships 4. Create common date tables 5. Implement row-level security roles 6. Create calculated tables and columns