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Microsoft Power BI Data Analyst Certification Exam is one of the most popular certification exams in the market. However, most candidates don't pass it on their first attempt. The reasons for this are many, but the most common ones are not having enough time to prepare, not knowing where to start and what to study, or not having access to quality learning materials. To help you overcome these obstacles and pass Microsoft Power BI Data Analyst Certification Exam easily, our experts have created a comprehensive Microsoft PL-300 Dumps that will give you everything you need to know to pass Microsoft Power BI Data Analyst Certification Exam quickly and efficiently.
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What is the exam cost of the Microsoft Power BI Data Analyst Certification Exam
The cost of the Microsoft Power BI Data Analyst Certification Exam is 165 USD.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300
Microsoft PL-300 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
| Manage and secure Power BI | 15-20% | - Manage workspaces and assets
- 1. Create and configure workspaces
- 2. Manage data refresh and scheduled updates
- 3. Publish, update, and organize reports/datasets
- 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
- Deploy and distribute content
- 1. Monitor usage and performance
- 2. Embed content in applications or websites
- 3. Share reports, dashboards, and apps
|
| Model the data | 25-30% | - Optimize model performance
- 1. Optimize relationships and calculations
- 2. Reduce cardinality and data size
- 3. Use aggregations and composite models
- Design data models
- 1. Implement role-playing dimensions
- 2. Create common date tables
- 3. Define tables, columns, and hierarchies
- 4. Create relationships: cardinality, cross-filter direction, security
- Create model calculations
- 1. Implement aggregation and time intelligence
- 2. Use DAX: calculated columns, calculated tables, measures
- 3. Use CALCULATE and filter context
|
| Prepare the data | 25-30% | - Load and configure data
- 1. Define table structures and properties
- 2. Configure data refresh settings
- 3. Integrate data from multiple sources
- 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. Choose storage modes: Import, DirectQuery, DirectLake
- 4. Create and modify parameters
- Profile and clean data
- 1. Evaluate data statistics and column properties
- 2. Manage data types, columns, and rows
- 3. Resolve inconsistencies, null values, and errors
- 4. Transform data using Power Query Editor
|
| Visualize and analyze data | 25-30% | - 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. Implement custom visuals and R/Python scripts
- 3. Create and manage bookmarks and tooltips
- Create reports and dashboards
- 1. Create dashboards and pinned elements
- 2. Apply filters, slicers, and drill-through
- 3. Choose appropriate visualizations
- 4. Design report layout, formatting, and interactivity
|