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What is the salary of the Microsoft Power BI Data Analyst Certified professional

The Average salary of different countries of Microsoft Power BI Data Analyst certified professionals:

Canada; CAD 70,000
India; INR 500,000
United Kingdom; POUNDS 60,000
Australia; AUD 75,000
United States; USD 90,000

Benefits

The Microsoft Power BI Data Analyst certification is a globally recognized certification that validates your ability to design, build, and optimize data models using Power BI. It also demonstrates your expertise in building queries and reports with appropriate visuals, including dashboards, scorecards, and visualizations. This certification focuses on the intermediate-level skills required for data modeling and analysis.

The Microsoft Power BI Data Analyst certification provides many benefits to you. It helps you boost your career growth by validating your knowledge in building business intelligence solutions using Power BI platform. It validates your skills in designing reports and visualizations for various business needs. It increases your credibility among employers as most companies look for professionals who have this certification. It provides an opportunity to earn a higher salary package compared to those without this certification. GuideTorrent Microsoft PL-300 Dumps will help you to pass your certification exam and get highly paid job.

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Understanding Microsoft Power BI Data Analyst Certification Exam Topics

Microsoft Power BI Data Analyst Certification Exam Topics are below:

Prepare the data (15-20%)
Deploy and maintain assets (20-25%)
Visualize and analyze the data (25-30%)
Model the data (30-35%)

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

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Microsoft PL-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Model the data	25-30%	- Create model calculations 1. Use CALCULATE and filter context 2. Implement aggregation and time intelligence 3. Use DAX: calculated columns, calculated tables, measures - Optimize model performance 1. Reduce cardinality and data size 2. Use aggregations and composite models 3. Optimize relationships and calculations - Design data models 1. Create relationships: cardinality, cross-filter direction, security 2. Implement role-playing dimensions 3. Create common date tables 4. Define tables, columns, and hierarchies
Topic 2: Visualize and analyze data	25-30%	- Perform data analysis 1. Implement custom visuals and R/Python scripts 2. Create and manage bookmarks and tooltips 3. Interpret results and identify trends - Enhance reports with analytics 1. Use AI visuals, forecasts, and key influencers 2. Apply conditional formatting and custom visuals 3. Create paginated reports - Create reports and dashboards 1. Apply filters, slicers, and drill-through 2. Choose appropriate visualizations 3. Design report layout, formatting, and interactivity 4. Create dashboards and pinned elements
Topic 3: Prepare the data	25-30%	- 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. Create and modify parameters 4. Choose storage modes: Import, DirectQuery, DirectLake - Profile and clean data 1. Resolve inconsistencies, null values, and errors 2. Evaluate data statistics and column properties 3. Transform data using Power Query Editor 4. Manage data types, columns, and rows - Load and configure data 1. Configure data refresh settings 2. Integrate data from multiple sources 3. Define table structures and properties
Topic 4: Manage and secure Power BI	15-20%	- Manage workspaces and assets 1. Create and configure workspaces 2. Publish, update, and organize reports/datasets 3. Manage data refresh and scheduled updates - Implement security and governance 1. Manage permissions and access 2. Apply data sensitivity labels and protection 3. Configure row-level security (RLS) and object-level security - Deploy and distribute content 1. Monitor usage and performance 2. Share reports, dashboards, and apps 3. Embed content in applications or websites