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What are the steps to follow for the registration of the Microsoft Power BI Data Analyst Certification Exam

The steps to follow for the registration of the Microsoft Power BI Data Analyst Certification Exam are as follows:

  • Go to the official website of Microsoft, and click on “Microsoft Learning”.

  • The next step is to fill out your personal information such as name, email address, country and state city etc., which is mandatory to complete your registration process successfully.

  • You will see a list of courses that are available on this website. Select “Microsoft Power BI Data Analyst Certification Exam” and click on “Enroll Now” button.

  • Then you need to select your preferred payment method which includes credit card or PayPal account etc., and you will receive an email confirmation after successful payment using your credit card or PayPal account etc.

  • After successful registration process, you will receive an email confirmation from Microsoft with all the details about your course including access code etc., which will be sent to your registered email ID provided during registration process.

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

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

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

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