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Microsoft Power BI Data Analyst Certification Exam Reference

Microsoft Power BI Data Analyst Exam Certification Details:

Exam Code	PL-300
Exam Name	Microsoft Certified - Power BI Data Analyst Associate
Duration	180 mins
Passing Score	700 / 1000
Number of Questions	40-60
Schedule Exam	Pearson VUE
Exam Price	$165 (USD)
Books / Training	Course PL-300T00: Microsoft Power BI Data Analyst
Sample Questions	Microsoft Power BI Data Analyst Sample Questions

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

Microsoft PL-300 Exam Syllabus Topics:

Section	Weight	Objectives
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. Create and manage bookmarks and tooltips 3. Implement custom visuals and R/Python scripts - Create reports and dashboards 1. Apply filters, slicers, and drill-through 2. Choose appropriate visualizations 3. Create dashboards and pinned elements 4. Design report layout, formatting, and interactivity
Manage and secure Power BI	15-20%	- Implement security and governance 1. Apply data sensitivity labels and protection 2. Configure row-level security (RLS) and object-level security 3. Manage permissions and access - Manage workspaces and assets 1. Manage data refresh and scheduled updates 2. Create and configure workspaces 3. Publish, update, and organize reports/datasets - Deploy and distribute content 1. Share reports, dashboards, and apps 2. Embed content in applications or websites 3. Monitor usage and performance
Model the data	25-30%	- 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. Use aggregations and composite models 2. Reduce cardinality and data size 3. Optimize relationships and calculations - 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
Prepare the data	25-30%	- Profile and clean data 1. Manage data types, columns, and rows 2. Transform data using Power Query Editor 3. Resolve inconsistencies, null values, and errors 4. Evaluate data statistics and column properties - Get or connect to data 1. Configure data source settings, credentials, and privacy levels 2. Create and modify parameters 3. Identify and connect to data sources or shared semantic models 4. Choose storage modes: Import, DirectQuery, DirectLake - Load and configure data 1. Integrate data from multiple sources 2. Define table structures and properties 3. Configure data refresh settings