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What are the steps to follow for the registration of the Microsoft Power BI Data Analyst Certification Exam

The steps to follow for the registration of the Microsoft Power BI Data Analyst Certification Exam are as follows:

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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

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Microsoft Power BI Data Analyst Exam Certification Details:

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

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

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Microsoft PL-300 Korean Exam Syllabus Topics:

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