PL-300日本語 exam dumps

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  • Exam Code: PL-300日本語
  • Exam Name: Microsoft Power BI Data Analyst (PL-300日本語版)
  • No. of Questions: 472 Questions and Answers
  • Updated: Sep 24, 2026

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There are many ways to get certified as a data analyst or BI professional. You can go through a formal education program or attend classes offered by various institutions and organizations. However, if you want to ensure that you get the best job opportunities and get hired easily, then getting certified is what you need to do. Here at ExamBoosts our Microsoft PL-300 Dumps will help you to get certified. Basic measures of column chart scenario have scenario based questions that shared dataset of calculated table.

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

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Microsoft Power BI Data Analyst Exam Certification Details:

Exam NameMicrosoft Certified - Power BI Data Analyst Associate
Number of Questions40-60
Books / TrainingCourse PL-300T00: Microsoft Power BI Data Analyst
Exam Price$165 (USD)
Exam CodePL-300
Schedule ExamPearson VUE
Duration180 mins
Sample QuestionsMicrosoft Power BI Data Analyst Sample Questions
Passing Score700 / 1000

What is the salary of the Microsoft Power BI Data Analyst Certified professional

The Average salary of different countries of Microsoft Power BI Data Analyst certified professionals:

  • United States; USD 90,000

  • United Kingdom; POUNDS 60,000

  • India; INR 500,000

  • Canada; CAD 70,000

  • Australia; AUD 75,000

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

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