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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.
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This exam measures your ability to accomplish the following technical tasks: prepare the data; model the data; visualize the data; analyze the data; and deploy and maintain deliverables.
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Exam DA-100: Analyzing Data with Microsoft Power BI
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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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
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Microsoft DA-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Visualize the data | 20-25% | - Create reports and dashboards - Enrich reports for usability - Apply filters, slicers, and formatting |
| Model the data | 25-30% | - Optimize model performance - Design and develop data models - Create measures using DAX - Create calculated tables and columns |
| Analyze the data | 10-15% | - Identify patterns and trends - Perform advanced analysis - Apply AI visuals and insights |
| Deploy and maintain deliverables | 10-15% | - Manage workspaces and datasets - Configure refresh and security - Share and distribute content |
| Prepare the data | 20-25% | - Clean, transform, and load data - Get data from different sources - Profile and examine data |
