Certification Opportunities
Upon completion of this single exam and obtaining the certification, the path doesn’t end. The candidates can pursue other Microsoft certificates to expand their competence area. These include the options such as Microsoft Certified: Azure Data Engineer Associate and Microsoft Certified: Azure Data Scientist Associate, to name a few.
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Conclusion: Getting Microsoft Certified Is Challenging But Possible!
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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However, if you would like to use different source data/tables in the same project, link them together and perform transformations on the data in Power BI as well, then the level of difficulty increases. Learning this will take of course more time. What we believe will also help is to get a general understanding of how data cleansing works and data integration in general. We offer MICROSOFT DA-100 practice exams and MICROSOFT DA-100 practice test for this purpose.
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Compared to other visualization tools we would recommend that Power BI is quite user friendly and is relatively easy to learn. What also contributes to this is that the Power BI desktop version is available for free, resulting in a pretty large user base on the internet, which is beneficial to your learning curve, as in our experience answers to particular questions can be found easily most of the times.
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 |
|---|---|---|
| Topic 1: Visualize the data | 25-30% | - Create reports
|
| Topic 2: Analyze the data | 10-15% | - Analyze data patterns
|
| Topic 3: Model the data | 25-30% | - Design data models
|
| Topic 4: Deploy and maintain deliverables | 10-15% | - Manage workspaces and datasets
|
| Topic 5: Prepare the data | 20-25% | - Clean and transform data
|







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