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Ideal Audience and Required Skills

DA-100 is intended for data analysts who use MS Power BI to enhance the value of the data their organization holds. These professionals are responsible for polishing and transforming data, creating expandable data models, and performing data visualizations that can be interpreted and analyzed to withdraw critical information. Data analysts also work alongside other stakeholders of the business to provide relevant data according to their company’s requirements. Thus, a strong prior understanding of both cloud & on-premises data processing and data repositories is also required by those specialists who are going to take test DA-100.

Microsoft DA-100 Exam Syllabus Topics:

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100

Career Opportunities and Pay

After getting through with DA-100 exam and earning the Microsoft Certified: Data Analyst Associate certification, you will be able to offer more for big business companies that need more analytical power that Excel cannot provide. This opens up multiple job opportunities with decent average annual salaries. Below are some of them as covered by Payscale:

• Web Data Analyst: $67k.
• IT Operation Analyst: $56k;
• Data Analyst: $61k;

Understanding functional and technical aspects of Exam DA-100: Analyzing Data with Microsoft Power BI Model the data

The following will be discussed in MICROSOFT DA-100 exam dumps:

• Create hierarchies
• Apply cross-filter direction and security filtering
• Use basic statistical functions to enhance data
• Identify poorly performing measures, relationships, and visuals
• Define the appropriate level of data granularity
• Define a relationship's cardinality and cross-filter direction
• Create measures by using DAX
• Use CALCULATE to manipulate filters
• Improve cardinality levels by changing data types
• Implement row-level security roles
• Optimize model performance
• Flatten out a parent-child hierarchy
• Remove unnecessary rows and columns
• Design a data model
• Create and manage aggregations
• Create calculated tables
• Define quick measures
• Set up the Q&A feature
• Configure table and column properties
• Create a common date table
• Improve cardinality levels through summarization
• Design the data model to meet performance requirements
• Create calculated columns
• Create semi-additive measures
• Define the tables
• Replace numeric columns with measures
• Develop a data model
• Define role-playing dimensions
• Use DAX to build complex measures
• Implement Time Intelligence using DAX
• Resolve many-to-many relationships