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

PL-300日本語 real exams

Exam Code: PL-300J

Exam Name: Microsoft Power BI Data Analyst (PL-300日本語版)

Updated: Sep 19, 2026

Q & A: 472 Questions and Answers

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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 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.

  • 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.

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

Learn more about the exam

The need for this exam has increased tremendously over the years. As more people start using technology, there is a greater need for more professionals who can help them with their problems. With the increasing demand for people with these skills, it will be difficult for you to get a job without having one. If you want to succeed in your career, you should consider taking this test because it will give you an edge over other candidates who do not have it. It will also help you stand out from the crowd if you are applying for jobs online or even if you are looking for promotion within your company. Column data types have questions and answers subcategories for real time scenario. Sample questions about data source settings have calculated columns mobile view. Quick measure mode filters the app syllabus to practice the average sales. If you are feeling sensitive to additives in your food, you may want to format your schema prepared environment to be more additive-sensitive. This could involve avoiding foods that contain additives, eating organic foods whenever possible, or using natural sweeteners.

Microsoft PL-300日本語 Exam Syllabus Topics:

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

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