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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.

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 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.

  • 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.

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

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Introduction of Microsoft PL-300 Exam

Microsoft offers a wide range of certifications to enhance your knowledge in various areas of IT. The Microsoft Power BI Data Analyst Certification Exam is one of the most popular and sought-after Microsoft certification exams which aims to develop your skills as a data analyst by providing you with an opportunity to learn about basic business intelligence concepts, data analysis techniques and tools that can be used for building data models. Microsoft PL-300 Dumps are the best way to pass your exam.

The Microsoft certification exams enable you to validate your knowledge and skills in the system. If you have a job, it helps you to get promoted. The certification is a must-have for IT professionals. Microsoft Power BI Data Analyst Certification Exam is an exam designed by Microsoft for data analysts who are interested in gaining knowledge of the Microsoft Azure cloud platform and Power BI suite tools. The exam tests your ability to design, develop, and implement analytical solutions using Excel, Power Pivot, Power View and other related tools.

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