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The candidates who want to earn the Microsoft Certified: Data Analyst Associate certification must pass one qualifying exam, which is Microsoft DA-100. This test evaluates the ability of the test takers to accomplish specific technical tasks. These include the processes with data, such as analyzing, preparing, maintaining and deploying, visualizing, and modeling.
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The Microsoft Certified: Data Analyst Associate certificate will help secure a career as a data analyst, which will come with it a high paycheck and different career opportunities. However, achieving this certification won’t be easy as one needs to overcome DA-100 (Analyzing Data with Microsoft Power BI) exam. But, with a huge number of study guides available on Amazon and the official MS training course, passing this test is going to be that much easier.
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The certifications from a renowned vendor such as Microsoft enhance the chances of the professionals to land a decent job. The individuals with the Microsoft Certified: Data Analyst Associate certificate are also at an advantage because this option validates their skills and competence in a specific technical domain. Some of the job titles that the certified specialists can pursue include a Data Analyst, a Business Analyst, a Mobile Test Analyst, a Revenue Analyst, and a Quantity Assurance Analyst. The average salary for these experts is $105,000 per annum.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
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The Microsoft DA-100: Analyzing Data with Microsoft Power BI exam measures the capability of the individuals to complete certain technical tasks. These include preparing data, visualizing data, modeling data, analyzing data, and maintaining and deploying deliverables. This is the only test that is required to earn the Microsoft Certified: Data Analyst Associate certification.
Microsoft DA-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model the data | 25-30% | - Design and develop data models - Create measures using DAX - Create calculated tables and columns - Optimize model performance |
| Topic 2: Visualize the data | 20-25% | - Enrich reports for usability - Create reports and dashboards - Apply filters, slicers, and formatting |
| Topic 3: Prepare the data | 20-25% | - Profile and examine data - Clean, transform, and load data - Get data from different sources |
| Topic 4: Deploy and maintain deliverables | 10-15% | - Manage workspaces and datasets - Configure refresh and security - Share and distribute content |
| Topic 5: Analyze the data | 10-15% | - Apply AI visuals and insights - Identify patterns and trends - Perform advanced analysis |






