Microsoft Microsoft Power BI Data Analyst (PL-300日本語版) - PL-300日本語 Valid Dumps

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

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  • 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.

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

  • 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.

  • 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.

  • 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.

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Microsoft PL-300日本語 Exam Syllabus Topics:

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

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