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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.

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Skills measured

  • Download the study guide in the preceding “Tip” box for more details about the skills measured on this exam.
  • Visualize and analyze the data (25-30%)
  • Prepare the data (15-20%)
  • Deploy and maintain assets (20-25%)
  • Model the data (30-35%)

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Prerequisites of Microsoft Power BI Data Analyst Certification Exam

The Microsoft Power BI Data Analyst Certification Exam is a test which assesses your ability to have a high-level understanding of the data analysis process, including collecting, analyzing and visualizing data. It is a certification that will help you gain the required skills to be able to work with business intelligence solutions and tools. To be eligible to take this exam, you must meet the following prerequisites:

  • You have at least one year of experience using Power Query in Excel, or an equivalent experience using another data analysis tool.

  • You have at least one year of experience working with relational databases, such as SQL Server or Oracle. In addition, you should have a basic understanding of how to create queries and reports using SQL Server Management Studio or Toad Data Point.

  • You are a professional data analyst with 2-4 years of work experience or an IT professional with 2-4 years of experience.

  • You have at least one year of experience using Power BI Desktop.

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

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Microsoft PL-300 Exam Syllabus Topics:

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

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