[NEW] Microsoft Power BI DA-100 Exam (Updated JAN 2022)

 

300+ MS POWER BI (DA-100) Exam Questions 2022 for you to successfully prepare and clear the exam on the first attempt
Description:

Welcome to the best practice exams to help you prepare for your Microsoft Power BI DA-100 exam

Get ready to pass the DA-100 exam right now using our Microsoft DA-100 exam package, which includes Microsoft DA-300 practice test ,  The best DA-100 exam study material and preparation tool is here.

Key Points:

1) Covers every topic on the DA-100 Skills Measured list
2) Direct access to live, Azure SQL Database with sample data
3) 30+ DA-100 Practice Exam Questions
4) Many hours of premium video tutorials from a Microsoft MVP

Course Content

1) Prepare the data (20-25%)
2) Model the data (25-30%)
3) Visualize the data (20-25%)
4) Analyze the data (10-15%)
5) Deploy and maintain deliverables (10-15%)

About Analyzing Data with Microsoft Power BI (DA-100) Exam

The Microsoft DA-100 exam enables Data Analysts to maximize the value of their data assets by using Microsoft Power BI. As a Data Analyst, your role will be to enable businesses to maximize the value of their data assets using Microsoft Power BI. Being subject matter expert, as a Data Analyst you will be responsible to perform the following tasks :

1) Designing and building scalable data models
2) Cleaning and transforming data
3) Enabling advanced analytic capabilities to provide meaningful business value using easy-to-comprehend data visualizations.

Also, as Data Analysts, you will be required to associate with key stakeholders across verticals to deliver relevant insights based on identified business requirements.

Recommended Knowledge

As a Data Analyst, it is suggested to have a fundamental understanding of data repositories and data processing both on-premises and in the cloud.

Skills Acquired

The Analyzing Data with Microsoft Power BI (DA-100) Exam has been built to measure your ability to accomplish technical tasks including -

1) Preparing the data
2) Modelling the data
3) Visualizing the data
4) Analyzing the data
5) Deploying and maintain deliverables

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