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Microsoft PL-300 is a certification exam that is designed for individuals who are interested in becoming a Microsoft Power BI data analyst. PL-300 exam is intended to test an individual's understanding of Power BI data manipulation and visualization, as well as their ability to develop and implement Power BI data models. The PL-300 certification exam is an important credential for individuals who are looking to advance their careers in the field of data analysis.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300
Microsoft PL-300 Certification Exam, also known as the Microsoft Power BI Data Analyst Certification Exam, is designed to test the skills and knowledge of individuals who use Microsoft Power BI to analyze data and create visualizations. PL-300 exam is intended for data analysts and business intelligence professionals who work with data to gain insights and make informed decisions.
It is a universally accepted fact that the PL-300 exam is a tough nut to crack for the majority of candidates, but there are still a lot of people in this field who long to gain the related certification so that a lot of people want to try their best to meet the challenge of the PL-300 exam. A growing number of people know that if they have the chance to pass the exam, they will change their present situation and get a more decent job in the near future. More and more people have realized that they need to try their best to prepare for the PL-300 exam.
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Microsoft PL-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Prepare the data | 25-30% | - Profile and clean data
- 1. Resolve inconsistencies, null values, and errors
- 2. Evaluate data statistics and column properties
- 3. Manage data types, columns, and rows
- 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. Identify and connect to data sources or shared semantic models
- 4. Configure data source settings, credentials, and privacy levels
|
| Manage and secure Power BI | 15-20% | - Manage workspaces and assets
- 1. Publish, update, and organize reports/datasets
- 2. Manage data refresh and scheduled updates
- 3. Create and configure workspaces
- 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. Monitor usage and performance
- 2. Share reports, dashboards, and apps
- 3. Embed content in applications or websites
|
| Model the data | 25-30% | - 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
- Create model calculations
- 1. Implement aggregation and time intelligence
- 2. Use DAX: calculated columns, calculated tables, measures
- 3. Use CALCULATE and filter context
- Optimize model performance
- 1. Optimize relationships and calculations
- 2. Use aggregations and composite models
- 3. Reduce cardinality and data size
|
| Visualize and analyze data | 25-30% | - Create reports and dashboards
- 1. Apply filters, slicers, and drill-through
- 2. Design report layout, formatting, and interactivity
- 3. Create dashboards and pinned elements
- 4. Choose appropriate visualizations
- Perform data analysis
- 1. Create and manage bookmarks and tooltips
- 2. Interpret results and identify trends
- 3. Implement custom visuals and R/Python scripts
- Enhance reports with analytics
- 1. Apply conditional formatting and custom visuals
- 2. Use AI visuals, forecasts, and key influencers
- 3. Create paginated reports
|