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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 BI Desktop.

  • 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 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.

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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

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

Microsoft PL-300 Korean Exam Syllabus Topics:

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

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