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The Microsoft Power BI Data Analyst certification is a globally recognized certification that validates your ability to design, build, and optimize data models using Power BI. It also demonstrates your expertise in building queries and reports with appropriate visuals, including dashboards, scorecards, and visualizations. This certification focuses on the intermediate-level skills required for data modeling and analysis.
The Microsoft Power BI Data Analyst certification provides many benefits to you. It helps you boost your career growth by validating your knowledge in building business intelligence solutions using Power BI platform. It validates your skills in designing reports and visualizations for various business needs. It increases your credibility among employers as most companies look for professionals who have this certification. It provides an opportunity to earn a higher salary package compared to those without this certification. Actual4Labs Microsoft PL-300 Dumps will help you to pass your certification exam and get highly paid job.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300
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Microsoft PL-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Model the Data | 25-30% | - Develop the data model
- 1. Configure table relationships
- 2. Use the calculation group feature
- 3. Build hierarchies and hierarchical fields
- 4. Implement row-level security roles
- 5. Create common date tables
- 6. Create calculated tables and columns
- Create measures and DAX calculations
- 1. Use Time Intelligence functions
- 2. Use CALCULATE and FILTER functions
- 3. Implement context transition
- 4. Write and use basic DAX syntax
- 5. Create calculated measures
- 6. Create semi-additive measures
- Optimize model performance
- 1. Optimize DirectQuery models
- 2. Configure aggregations
- 3. Validate performance and resolve issues
- 4. Optimize Dax queries
- 5. Improve user experience by hiding objects
- Design a data model
- 1. Apply field metadata and groupings
- 2. Resolve performance issues with relationships
- 3. Implement row-level security
- 4. Design a star schema or snowflake schema
- 5. Define tables and table schemas
- 6. Determine the appropriate granularity
|
| Topic 2: Analyze the Data | 10-15% | - Perform analysis in Power BI
- 1. Apply Quick Insights
- 2. Identify patterns and trends
- 3. Generate Pinn visual insights
- 4. Configure auto-insights
- 5. Use the Q&A visual
- Work with metrics in Power BI
- 1. Track goals with scorecards
- 2. Define and monitor KPIs
- 3. Create metrics using Microsoft measurements
- 4. Use the Decomposition Tree visual
|
| Topic 3: Manage and Maintain Workspace Items | 10-15% | - Create and manage workspaces
- 1. Configure content certification
- 2. Endorse sensitive content with sensitivity labels
- 3. Publish and update a Power BI app
- 4. Configure workspace membership and roles
- Manage datasets
- 1. Manage dataset refresh scheduling
- 2. Resolve dataset permission issues
- 3. Configure dataset metadata and properties
- 4. Manage gateways and data sources
|
| Topic 4: Visualize the Data | 25-30% | - Enrich reports with advanced visuals
- 1. Integrate Microsoft Copilot for Power BI
- 2. Create custom visuals
- 3. Use dynamic page navigation
- 4. Configure R and Python visuals
- Build visuals
- 1. Apply sorting and filtering to visuals
- 2. Format and configure pages and report backgrounds
- 3. Configure visual interactions and cross-filtering
- 4. Determine the appropriate visual type
- 5. Apply AI insights and enhanced analytics
- 6. Configure data colors and shapes
- 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
|
| Topic 5: Prepare the Data | 10-15% | - Get data from various data sources
- 1. Configure dataset refresh
- 2. Use Microsoft Fabric and OneLake
- 3. Choose between DirectQuery, Import, and Dual storage modes
- 4. Identify and connect to a data source
- 5. Select a shared dataset or create a local copy
- 6. Change data source settings
- Clean, transform, and load the data
- 1. Identify the data structure
- 2. Apply user-friendly naming conventions to columns and tables
- 3. Design and build intermediate tables
- 4. Resolve data quality issues
- 5. Profile the data
- 6. Configure data loading and queries
- 7. Determine proper data types
|