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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.
Part of the requirements for: Microsoft Certified: Power BI Data Analyst Associate
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Microsoft PL-300 Deutsch Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Visualize the Data | 25-30% | - Build visuals
- 1. Apply AI insights and enhanced analytics
- 2. Configure data colors and shapes
- 3. Apply sorting and filtering to visuals
- 4. Determine the appropriate visual type
- 5. Format and configure pages and report backgrounds
- 6. Configure visual interactions and cross-filtering
- Enrich reports with advanced visuals
- 1. Create custom visuals
- 2. Configure R and Python visuals
- 3. Integrate Microsoft Copilot for Power BI
- 4. Use dynamic page navigation
- Enhance reports for user experience
- 1. Apply artistic formatting to visuals
- 2. Add accessibility features and compliance
- 3. Add tooltips and navigation
- 4. Design for mobile and specific layouts
|
| Topic 2: Analyze the Data | 10-15% | - Work with metrics in Power BI
- 1. Create metrics using Microsoft measurements
- 2. Track goals with scorecards
- 3. Define and monitor KPIs
- 4. Use the Decomposition Tree visual
- Perform analysis in Power BI
- 1. Use the Q&A visual
- 2. Identify patterns and trends
- 3. Apply Quick Insights
- 4. Generate Pinn visual insights
- 5. Configure auto-insights
|
| Topic 3: Manage and Maintain Workspace Items | 10-15% | - Manage datasets
- 1. Manage dataset refresh scheduling
- 2. Manage gateways and data sources
- 3. Configure dataset metadata and properties
- 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 4: Prepare the Data | 10-15% | - Get data from various data sources
- 1. Identify and connect to a data source
- 2. Use Microsoft Fabric and OneLake
- 3. Select a shared dataset or create a local copy
- 4. Configure dataset refresh
- 5. Choose between DirectQuery, Import, and Dual storage modes
- 6. Change data source settings
- Clean, transform, and load the data
- 1. Resolve data quality issues
- 2. Configure data loading and queries
- 3. Identify the data structure
- 4. Apply user-friendly naming conventions to columns and tables
- 5. Profile the data
- 6. Design and build intermediate tables
- 7. Determine proper data types
|
| Topic 5: Model the Data | 25-30% | - Create measures and DAX calculations
- 1. Implement context transition
- 2. Write and use basic DAX syntax
- 3. Create semi-additive measures
- 4. Use Time Intelligence functions
- 5. Use CALCULATE and FILTER functions
- 6. Create calculated measures
- Develop the data model
- 1. Build hierarchies and hierarchical fields
- 2. Use the calculation group feature
- 3. Create calculated tables and columns
- 4. Configure table relationships
- 5. Create common date tables
- 6. Implement row-level security roles
- Optimize model performance
- 1. Optimize DirectQuery models
- 2. Improve user experience by hiding objects
- 3. Configure aggregations
- 4. Validate performance and resolve issues
- 5. Optimize Dax queries
- Design a data model
- 1. Apply field metadata and groupings
- 2. Implement row-level security
- 3. Design a star schema or snowflake schema
- 4. Resolve performance issues with relationships
- 5. Define tables and table schemas
- 6. Determine the appropriate granularity
|