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4. Data Analysis (10-15%):

This section requires your skills in:

  • 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.
  • 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.

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

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About Exam DA-100: Analyzing Data with Microsoft Power BI

The Microsoft DA-100 test is for those individuals who have at least some experience in working with Power Bi although it is not a strict requirement but a recommendation. In addition, the applicant should have some experience in the concepts of scaling data models, data visualizations, data cleaning, and data processing. When it comes to the test details, DA-100 exam is available in English, Chinese (Simplified), Korean, Japanese and will require $165 for registration. The registered candidate can use the Pearson VUE platform for scheduling an exam. In the final test, individuals will experience around 40-60 items that will have to be solved in 3 hours.

Microsoft DA-100 Deutsch Exam Syllabus Topics:

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

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