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What should you know about the Microsoft DA-100 exam before going for it?
The intended candidates for the Microsoft DA-100 exam are Data Analysts. These professionals help the organizations make the most of the value of data assets through the use of Microsoft Power BI. They also collaborate with the major stakeholders across the company to deliver the appropriate insights based on the recognized business prerequisites.
Microsoft DA-100 is an associate-level certification test, and as such, it does not have any official prerequisites. However, Microsoft recommends that the students have a good fundamental knowledge of data processing and data repositories both in Cloud and on-premises. The candidates for the exam should have the relevant skills in building and designing scalable data models, facilitating advanced analytic abilities, and transforming and cleaning data to provide significant business value through understandable data visualizations.
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Understanding functional and technical aspects of Exam DA-100: Analyzing Data with Microsoft Power BI Deploy and maintain deliverables
The following will be discussed in MICROSOFT DA-100 exam dumps:
- Configure and update a workspace app
- Manage datasets
- Configure incremental refresh settings
- Providing access to datasets
- Publish, import, or update assets in a workspace
- Recommend a development lifecycle strategy
- Assign workspace roles
- Create and configure a workspace
- Configure row-level security group membership
- Promote or certify Power BI content
- Configure a dataset scheduled refresh
- Create and manage workspaces
- Apply sensitivity labels to workspace content
- Identify downstream dataset dependencies
- Use deployment pipelines
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: Model the data | 25-30% | - Create calculated columns and measures
|
| Topic 2: Prepare the data | 20-25% | - Clean and transform data
|
| Topic 3: Visualize the data | 25-30% | - Enhance reports
|
| Topic 4: Analyze the data | 10-15% | - Analyze data patterns
|
| Topic 5: Deploy and maintain deliverables | 10-15% | - Manage workspaces and datasets
|



