Understanding functional and technical aspects of Exam DA-100: Analyzing Data with Microsoft Power BI Prepare the data
The following will be discussed in MICROSOFT DA-100 exam dumps:
- Interrogate data statistics
- Use or create a data flow
- Use or create a PBIDS file
- Identify query performance issues
- Apply data shape transformations to table structures
- Evaluate and transform column data types
- Identify data anomalies
- Combine queries
- Change data source settings
- Use Microsoft Dataverse
- Leverage Advanced Editor to modify Power Query M code
- Configure data loading
- Use parameters
- Examine data structures
- Choose an appropriate query type
- Select a storage mode
- Identify and connect to a data source
- Select a shared dataset or create a local dataset
- Interrogate column properties
- Identify and create appropriate keys for joins
- Resolve inconsistencies, unexpected or null values, and data quality issues
- Apply user-friendly naming conventions to columns and queries
- Apply user-friendly value replacements
- Resolve data import errors
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
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About Data Analyst Associate and Who It Is Intended for
The Microsoft Certified: Data Analyst Associate is earned by passing DA-100 and is a role-based intermediate-level certification that falls under the Microsoft Power Platform certification hierarchy and authenticates the data analyst's expertise in utilizing Microsoft Power BI in maximizing the businesses' data assets value. The earner of this certificate will be competent to design and develop scalable data models along with cleaning and transformation of data to provide business values through data visualizations. Overall, this certification will be beneficial for data analysts in broadening their technological prowess and the values that they provide for businesses.
Microsoft DA-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prepare the data | 20-25% | - Clean, transform, and load data - Get data from different sources - Profile and examine data |
| Visualize the data | 20-25% | - Create reports and dashboards - Enrich reports for usability - Apply filters, slicers, and formatting |
| Model the data | 25-30% | - Design and develop data models - Optimize model performance - Create calculated tables and columns - Create measures using DAX |
| Analyze the data | 10-15% | - Perform advanced analysis - Identify patterns and trends - Apply AI visuals and insights |
| Deploy and maintain deliverables | 10-15% | - Manage workspaces and datasets - Share and distribute content - Configure refresh and security |
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