The Microsoft Corporation is a reputed American multinational IT company that develops, manufactures, licenses supports, and sells consumer electronics, computer software, personal computers, and related services. To work on Microsoft technology, platform, and products a high level of expertise and knowledge are required. To fulfill this demand Microsoft offers an array of role-based certifications. These results-oriented certifications are designed to polish the expertise of aspirant Microsoft professionals. Everyone can participate in these certifications and become a certified Microsoft professional.
General Information of Microsoft DA-100 Exam
.Today we talk about one of the most demanding Microsoft DA-100 exam or Microsoft Analyzing Data with Microsoft Power BI exams. It is a professional-level certification that is designed for data analysts, data modelers, visualizers,s and Power BI Admin. With the DA-100 exam data experts can validate technical skill set and knowledge with data modeling concepts, data architecture, start schema, performance tuning, ETL concepts, and experience with visualization. The candidate should have a fundamental knowledge of data processing and data repositories on the cloud and premises. It is a good idea to get basic information about the Microsoft DA-100 exam so that you can prepare yourself in a perfect way.
| Exam Code | DA-100 |
| Exam Name | Analyzing Data with Microsoft Power BI |
| Exam Type | Cloud Computing |
| Number of Questions | 40-60 Questions |
| Exam Questions Format | Multiple Choice and Multi Response Questions |
| Exam Language | English, Japanese, Chinese (Simplified) and Korean |
| Exam Registration | $165 USD |
| Retirement Date | None |
As per the Microsoft official site, the Microsoft DA-100 exam measures your expertise about the designing of data, preparation of data, model of data, visualize the data, analyzing, deploying, and maintaining the data. Look at the topics and their percentage that is given in the following lines.
- Prepare the data (20-25%)
- Model the data (25-30%)
- Visualize the data (20-25%)
- Analyze the data (10-15%)
- Deploy and maintain deliverables (10-15%)
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