Multimatics

Academy | Syllabus

Microsoft Certified: Power BI Data Analyst Associate

This program will demonstrate methods and best practices that align with business and technical requirements for modeling, visualizing, and analyzing data with Microsoft Power BI. Participants will learn how to provide meaningful business value through easy-to-comprehend data visualizations, enable others to perform self-service analytics, deploy and configure solutions for consumption.

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Program Details

Durations

 The program is a 5-day intensive training class. 

Method of Delivery

The program provided by Multimatics will be delivered through interactive presentation by professional instructor(s), group debriefs, individual and team exercises, behavior modelling and roleplays, one-to-one and group discussion, case studies, and projects.

Who Should Attend?

The target audience for Microsoft Power BI is broad, encompassing both technical users (like data analysts, data scientists, developers, and IT administrators) who build solutions and business users (like managers and department heads) who need to interpret data and dashboards to make decisions.

Program Objectives

By the end of the program, participants will be able to:

  • Prepare the data
  • Model the data
  • Visualize and analyze the data
  • Manage and secure Power BI

Examination Details

  • Exam Name: PL-300: Microsoft Power BI Data Analyst Associate
  • Duration: 100 minutes
  • Number of Questions: Approximately 50
  • Passing Score: 700 out of 1,000
  • Question Types: Multiple choice, true/false, drag and drop, and case studies
  • Delivery: Available online or in person through a proctored exam
  • Cost: Typically around $165 USD for associate-level exams, subject to local pricing and taxes
  • Open-book: Yes, access to Microsoft Learn documentation is available during the exam 

Program Modules

Prepare the Data

Topic Covered

  • Get or connect to data
  • Identify and connect to data sources or a shared semantic model
  • Change data source settings, including credentials and privacy levels
  • Choose between DirectQuery and Import
  • Create and modify parameters
  • Profile and clean the data
  • Evaluate data, including data statistics and column properties
  • Resolve inconsistencies, unexpected or null values, and data quality issues
  • Resolve data import errors
  • Transform and load the data
  • Select appropriate column data types
  • Create and transform columns
  • Group and aggregate rows
  • Pivot, unpivot, and transpose data
  • Convert semi-structured data to a table
  • Create fact tables and dimension tables
  • Identify when to use reference or duplicate queries and the resulting impact
  • Merge and append queries
  • Identify and create appropriate keys for relationships
  • Configure data loading for queries