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Exam DA-100: Analyzing Data with Microsoft Power BI

Data Analysts enable businesses to maximize the value of their data assets by using Microsoft Power BI. As a subject matter expert, Data Analysts are responsible for designing and building scalable data models, cleaning and transforming data, and enabling advanced analytic capabilities that provide meaningful business value through easy-to-comprehend data visualizations. Data Analysts also collaborate with key stakeholders across verticals to deliver relevant insights based on identified business requirements.

The Data Analyst should have a fundamental understanding of data repositories and data processing both on-premises and in the cloud.

Part of the requirements for: Microsoft Certified: Data Analyst Associate

Download exam skills outline

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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:

  • Assign workspace roles
  • Use deployment pipelines
  • Create and manage workspaces
  • Configure and update a workspace app
  • Configure row-level security group membership
  • Promote or certify Power BI content
  • Configure incremental refresh settings
  • Recommend a development lifecycle strategy
  • Identify downstream dataset dependencies
  • Apply sensitivity labels to workspace content
  • Publish, import, or update assets in a workspace
  • Manage datasets
  • Create and configure a workspace
  • Providing access to datasets
  • Configure a dataset scheduled refresh

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

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Microsoft DA-100 Deutsch Exam Syllabus Topics:

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

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