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Analyzing Data with Microsoft Power BI Exam Certification Details:
| Exam Code | DA-100 |
| Books / Training | DA-100T00-A: Analyzing Data with Power BI |
| Exam Price | $165 (USD) |
| Number of Questions | 40-60 |
| Schedule Exam | Pearson VUE |
| Passing Score | 700 / 1000 |
| Sample Questions | Analyzing Data with Microsoft Power BI Sample Questions |
| Duration | 180 mins |
| Exam Name | Microsoft Certified - Data Analyst Associate |
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What is the cost of Exam DA-100: Analyzing Data with Microsoft Power BI
The cost of Exam DA-100: Analyzing Data with Microsoft Power BI is $165.
- Length of Examination: 90 minutes
- Format: Multiple choices, multiple answers
- Number of Questions: 40
- Passing Score: 70%
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
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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:
- Use parameters
- Interrogate data statistics
- Identify data anomalies
- Configure data loading
- Select a shared dataset or create a local dataset
- Combine queries
- Use or create a data flow
- Resolve inconsistencies, unexpected or null values, and data quality issues
- Apply data shape transformations to table structures
- Apply user-friendly value replacements
- Identify and create appropriate keys for joins
- Evaluate and transform column data types
- Leverage Advanced Editor to modify Power Query M code
- Identify query performance issues
- Interrogate column properties
- Examine data structures
- Change data source settings
- Use or create a PBIDS file
- Select a storage mode
- Use Microsoft Dataverse
- Resolve data import errors
- Identify and connect to a data source
- Choose an appropriate query type
- Apply user-friendly naming conventions to columns and queries
Microsoft DA-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Analyze the data | 10-15% | - Analyze data patterns
|
| Deploy and maintain deliverables | 10-15% | - Manage workspaces and datasets
|
| Model the data | 25-30% | - Create calculated columns and measures
|
| Prepare the data | 20-25% | - Get data from data sources
|
| Visualize the data | 25-30% | - Create reports
|








