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Exam Code: DA-100
Exam Name: Analyzing Data with Microsoft Power BI (DA-100日本語版)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified: Data Analyst Associate
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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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For more info about Exam DA-100: Analyzing Data with Microsoft Power BI

Exam DA-100: Analyzing Data with Microsoft Power BI

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100

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Microsoft DA-100日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Visualize the data25%- Create dashboards
  • 1. Configure data alerts
  • 2. Manage tiles on a dashboard
  • 3. Use the Q&A feature
  • 4. Set mobile view
- Create reports
  • 1. Format and configure visualizations
  • 2. Import a custom visual
  • 3. Add visualization items to reports
  • 4. Choose an appropriate visualization type
  • 5. Configure conditional formatting
  • 6. Create and manage bookmarks
  • 7. Apply slicing and filtering
- Enforce accessibility
  • 1. Configure tab order
  • 2. Use accessibility features
  • 3. Design accessible reports
Topic 2: Model the data30%- Develop a data model
  • 1. Create hierarchies
  • 2. Implement row-level security roles
  • 3. Create calculated tables
  • 4. Create calculated columns
- Design a data model
  • 1. Define a relationship's cross-filter direction and security filtering
  • 2. Design and implement role-playing dimensions
  • 3. Define the tables
  • 4. Define quick measures
  • 5. Configure table and column properties
- Create measures by using DAX
  • 1. Create semi-additive measures
  • 2. Implement Time Intelligence using DAX
  • 3. Use DAX aggregation functions
  • 4. Create calculated measures
  • 5. Use DAX logical functions
- Optimize model performance
  • 1. Improve performance by choosing optimal data types
  • 2. Improve performance by identifying and removing unnecessary rows and columns
  • 3. Optimize Power Query transformations
Topic 3: Analyze the data15%- Perform advanced analysis
  • 1. Use advanced analytical custom visuals
  • 2. Perform What-if analysis
  • 3. Use the Analyze feature
  • 4. Identify outliers
- Enhance reports for usability and storytelling
  • 1. Configure sync slicers
  • 2. Create custom tooltips
  • 3. Edit and configure interactions between visuals
  • 4. Configure navigation for a report
  • 5. Apply sorting
  • 6. Configure bookmarks
  • 7. Group and layer visuals
Topic 4: Deploy and maintain deliverables10%- Create and manage workspaces
  • 1. Assign workspace roles
  • 2. Configure and update a workspace app
  • 3. Create and configure a workspace
- Manage datasets
  • 1. Configure a dataset scheduled refresh
  • 2. Configure row-level security group membership
  • 3. Provide access to datasets
  • 4. Configure cross-workspace sharing
Topic 5: Prepare the data20%- Profile the data
  • 1. Interpret data distribution and variance
  • 2. Examine data structures
  • 3. Identify data anomalies
- Get data from different data sources
  • 1. Identify and connect to a data source
  • 2. Select a storage mode
  • 3. Select a shared dataset or create a local dataset
  • 4. Choose an appropriate Power BI data source
  • 5. Change data source settings
- Clean the data
  • 1. Evaluate data, including data statistics and column properties
  • 2. Resolve data import errors
  • 3. Resolve inconsistencies, unexpected or null values, and data quality issues
- Transform and load the data
  • 1. Define relationship cardinality and cross-filter direction
  • 2. Create and transform columns
  • 3. Create and transform tables
  • 4. Select appropriate column data types
  • 5. Design a star schema

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