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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. ActualVCE Microsoft PL-300 Dumps will help you to pass your certification exam and get highly paid job.

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What is the exam cost of the Microsoft Power BI Data Analyst Certification Exam

The cost of the Microsoft Power BI Data Analyst Certification Exam is 165 USD.

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

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Microsoft PL-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Analyze the Data10-15%- Perform analysis in Power BI
  • 1. Configure auto-insights
  • 2. Apply Quick Insights
  • 3. Generate Pinn visual insights
  • 4. Use the Q&A visual
  • 5. Identify patterns and trends
- Work with metrics in Power BI
  • 1. Track goals with scorecards
  • 2. Create metrics using Microsoft measurements
  • 3. Use the Decomposition Tree visual
  • 4. Define and monitor KPIs
Topic 2: Prepare the Data10-15%- Clean, transform, and load the data
  • 1. Profile the data
  • 2. Apply user-friendly naming conventions to columns and tables
  • 3. Identify the data structure
  • 4. Resolve data quality issues
  • 5. Determine proper data types
  • 6. Configure data loading and queries
  • 7. Design and build intermediate tables
- Get data from various data sources
  • 1. Select a shared dataset or create a local copy
  • 2. Use Microsoft Fabric and OneLake
  • 3. Choose between DirectQuery, Import, and Dual storage modes
  • 4. Configure dataset refresh
  • 5. Change data source settings
  • 6. Identify and connect to a data source
Topic 3: Manage and Maintain Workspace Items10-15%- Manage datasets
  • 1. Manage gateways and data sources
  • 2. Configure dataset metadata and properties
  • 3. Manage dataset refresh scheduling
  • 4. Resolve dataset permission issues
- Create and manage workspaces
  • 1. Configure workspace membership and roles
  • 2. Publish and update a Power BI app
  • 3. Configure content certification
  • 4. Endorse sensitive content with sensitivity labels
Topic 4: Visualize the Data25-30%- 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
- Build visuals
  • 1. Configure data colors and shapes
  • 2. Determine the appropriate visual type
  • 3. Apply sorting and filtering to visuals
  • 4. Configure visual interactions and cross-filtering
  • 5. Apply AI insights and enhanced analytics
  • 6. Format and configure pages and report backgrounds
- Enrich reports with advanced visuals
  • 1. Integrate Microsoft Copilot for Power BI
  • 2. Use dynamic page navigation
  • 3. Create custom visuals
  • 4. Configure R and Python visuals
Topic 5: Model the Data25-30%- Develop the data model
  • 1. Create common date tables
  • 2. Create calculated tables and columns
  • 3. Configure table relationships
  • 4. Use the calculation group feature
  • 5. Build hierarchies and hierarchical fields
  • 6. Implement row-level security roles
- Design a data model
  • 1. Design a star schema or snowflake schema
  • 2. Determine the appropriate granularity
  • 3. Implement row-level security
  • 4. Apply field metadata and groupings
  • 5. Define tables and table schemas
  • 6. Resolve performance issues with relationships
- Create measures and DAX calculations
  • 1. Create calculated measures
  • 2. Use Time Intelligence functions
  • 3. Use CALCULATE and FILTER functions
  • 4. Create semi-additive measures
  • 5. Write and use basic DAX syntax
  • 6. Implement context transition
- Optimize model performance
  • 1. Validate performance and resolve issues
  • 2. Improve user experience by hiding objects
  • 3. Configure aggregations
  • 4. Optimize DirectQuery models
  • 5. Optimize Dax queries

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