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PDF version, Soft version, APP version
DA-100 Deutsch certification training materials have three different formats with same questions and answers. Users can choose the suited version as you like. PDF version of DA-100 Deutsch training materials is familiar by most learners. You can read it on any device or print out as paper files. If you like studying and noting on paper, PDF version of DA-100 Deutsch study materials: Analyzing Data with Microsoft Power BI (DA-100 Deutsch Version) is the right option for you. Soft version & APP version have similar functions such as simulating the real exam scene. The difference is that soft version of DA-100 Deutsch certification training is only used on windows & Java system, the app version is available for all devices. You can use both of them without any use limitation of time, place or the number of times.
4. Data Analysis (10-15%):
This section requires your skills in:
- 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.
- 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.
If you are a data analyst striving to get your skills validated in the scope of data assets, transformations, and models, then the Microsoft DA-100 exam is the one that will assist you with this.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
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Microsoft DA-100 Deutsch Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare the data | 20% | - Profile the data
|
| Topic 2: Visualize the data | 25% | - Enforce accessibility
|
| Topic 3: Analyze the data | 15% | - Perform advanced analysis
|
| Topic 4: Model the data | 30% | - Optimize model performance
|
| Topic 5: Deploy and maintain deliverables | 10% | - Manage datasets
|


