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Exam Code: PL-300
Exam Name: Microsoft Power BI Data Analyst (PL-300日本語版)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified: Power BI Data Analyst Associate

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PL-300日本語

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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

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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 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 Query in Excel, or an equivalent experience using another data analysis tool.

Microsoft PL-300日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prepare the data25-30%- Profile and clean data
  • 1. Resolve inconsistencies, null values, and errors
    • 2. Manage data types, columns, and rows
      • 3. Evaluate data statistics and column properties
        • 4. Transform data using Power Query Editor
          - Get or connect to data
          • 1. Identify and connect to data sources or shared semantic models
            • 2. Configure data source settings, credentials, and privacy levels
              • 3. Create and modify parameters
                • 4. Choose storage modes: Import, DirectQuery, DirectLake
                  - Load and configure data
                  • 1. Configure data refresh settings
                    • 2. Define table structures and properties
                      • 3. Integrate data from multiple sources
                        Topic 2: Visualize and analyze data25-30%- Enhance reports with analytics
                        • 1. Create paginated reports
                          • 2. Use AI visuals, forecasts, and key influencers
                            • 3. Apply conditional formatting and custom visuals
                              - Create reports and dashboards
                              • 1. Choose appropriate visualizations
                                • 2. Create dashboards and pinned elements
                                  • 3. Design report layout, formatting, and interactivity
                                    • 4. Apply filters, slicers, and drill-through
                                      - Perform data analysis
                                      • 1. Implement custom visuals and R/Python scripts
                                        • 2. Interpret results and identify trends
                                          • 3. Create and manage bookmarks and tooltips
                                            Topic 3: Manage and secure Power BI15-20%- Implement security and governance
                                            • 1. Manage permissions and access
                                              • 2. Apply data sensitivity labels and protection
                                                • 3. Configure row-level security (RLS) and object-level security
                                                  - Deploy and distribute content
                                                  • 1. Embed content in applications or websites
                                                    • 2. Monitor usage and performance
                                                      • 3. Share reports, dashboards, and apps
                                                        - Manage workspaces and assets
                                                        • 1. Create and configure workspaces
                                                          • 2. Manage data refresh and scheduled updates
                                                            • 3. Publish, update, and organize reports/datasets
                                                              Topic 4: Model the data25-30%- Create model calculations
                                                              • 1. Implement aggregation and time intelligence
                                                                • 2. Use DAX: calculated columns, calculated tables, measures
                                                                  • 3. Use CALCULATE and filter context
                                                                    - Design data models
                                                                    • 1. Implement role-playing dimensions
                                                                      • 2. Create relationships: cardinality, cross-filter direction, security
                                                                        • 3. Create common date tables
                                                                          • 4. Define tables, columns, and hierarchies
                                                                            - Optimize model performance
                                                                            • 1. Reduce cardinality and data size
                                                                              • 2. Optimize relationships and calculations
                                                                                • 3. Use aggregations and composite models

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