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The Microsoft Power BI Data Analyst Certification Exam will help you prepare for the new job role of a data analyst. A certified Data Analyst can use this exam to prove their skills in using Power BI to analyze business data, generate insightful visualizations, and share them with customers. Microsoft PL-300 Dumps is designed to test your knowledge of business data modeling, data analysis and visualization, and dashboard development in Power BI. The exam will cover the following topics:

  • Creating custom data models that support analysis and reporting requirements

  • Developing dashboards that provide actionable insights into business performance

  • Identifying sources of data that can contribute to achieving business goals

  • Understanding business context, including organizational strategy and objectives

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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

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

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