4. Data Analysis (10-15%):
This section requires your skills in:
- Enhancing reports to expose insights: The candidates should have the expertise in personalizing visuals, applying filters & slicers and conditional formatting. They also need to demonstrate their skills in exploring statistical summary, creating reference lines with Analytics pane, and using Q&A visuals, among others.
- Caring out advanced analysis: This subject area covers your skills in identifying outliers, using binnings & groupings, conducting Time Series Analysis, and applying AI Insights. The students should also have competence in using decomposition tree visuals to dissect a measure and use Key Influencers for the exploration of dimensional variances.
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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
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What is the Microsoft DA-100 exam?
The Microsoft DA-100 exam is available in English, Japanese, Simplified Chinese, and Korean. The learners can expect 40-60 questions in the delivery of the test with the time allowance of 180 minutes. Depending on where they are taking the exam, it is available as a proctored option or at any Pearson VUE testing center. The candidates can register for this certification exam with Pearson VUE and pay the fee of $165 to schedule it. This price is established for the individuals from the United States. If you sit for the test in another country, you should check the actual price on the official website.
Microsoft DA-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Visualize the data | 25-30% | - Enhance reports
|
| Deploy and maintain deliverables | 10-15% | - Secure and share content
|
| Analyze the data | 10-15% | - Perform advanced analytics
|
| Model the data | 25-30% | - Create calculated columns and measures
|
| Prepare the data | 20-25% | - Get data from data sources
|



