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Last Updated: Aug 21, 2026

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Analyzing Data with Microsoft Power BI Exam Certification Details:

Exam NameMicrosoft Certified - Data Analyst Associate
Number of Questions40-60
Sample QuestionsAnalyzing Data with Microsoft Power BI Sample Questions
Passing Score700 / 1000
Exam Price$165 (USD)
Books / TrainingDA-100T00-A: Analyzing Data with Power BI
Exam CodeDA-100
Duration180 mins
Schedule ExamPearson VUE

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Exam DA-100 Topics Covered

Test DA-100 measures the entrant's skills in 5 different domains:

  • Modeling the data (25-30%)

    This part of the final exam includes designing a data model. Here, the candidate should be able to define and configure tables along with having an idea about various dimensions and relationships. They are also required to be familiar with the concept of cardinality and cross-filter direction. After designing a data model, the applicant should be aware of how to develop it. Therefore, understanding of security filtering, calculated tables, hierarchies, and the implementation of row-level security roles will be beneficial. Furthermore, the entrants' expertise in working with DAX will be tested in this sector. They are required to be capable of using DAX for building complex measures, taking advantage of CALCULATE for manipulating filters using DAX with Time Intelligence, and more. In addition, this portion of DA-100 also involves the optimization of model performance by removing rows and columns, changing data types, managing aggregations, etc. Finally, the entrant should also know how to create semi-additive measures.

  • Analyzing the data (10-15%)

    In the fourth tested area, the candidate will be asked to enhance reports for exposing insights. This means complying with the need to be well-informed about conditional formatting, slicers, filters, Analytics pane along with a few other concepts. Besides, candidates must be able to add Quick Insights results to reports and use Q&A visuals. Likewise, the exam-taker should be able to know how to execute the advanced analysis. This involves the understanding of outliers, Timer Series analysis, groupings, binning, dimensional variances, decompositions tree visuals, and AI Insights. The exam also requires an understanding of the Play Axis feature and the personalization of visuals.

  • Visualizing the data (20-25%)

    The third domain will test the applicant's know-how of creating reports, dashboards, and enriching reports for usability. This will require the candidate's expertise in adding visualization to reports, choosing a proper visualization style, formatting visualizations, slicing and filtering applications, configuring a report page, building automatic page refresh, etc. The knowledge of R and Python visuals will also be beneficial. Furthermore, it is also essential for the entrant to know how to set dashboard to mobile view, manage tiles, configure data alerts, arrange aspects of the dashboard, and pin live reports. Finally, the applicant needs to enrich the reports by configuring bookmarks, creating custom tooltips, sorting & configuring various Sync Slicers, designing reports for mobile devices, etc.

  • Deployment and maintenance deliverables (10-15%)

    The fifth and final section of DA-100 exam includes managing datasets. The candidate must be able to configure dataset scheduled refresh and row-level security. They are also expected to have an idea about giving access to data sets, incremental refresh settings, and certifying datasets. Moreover, the entrant is required to be well-informed about creating and managing workspaces. They should be able to create and configure workspaces, assign roles in the workspace, and manage the workspace app. Knowing how to import, export, update, and publish workspace assets will be essential. Finally, the candidate needs to know about applying sensitivity labels in workspace contents. The entrant also needs to be capable of identifying downstream data set dependencies and using deployment pipelines.

  • Preparation of data (20-25%)

    The first domain for DA-100 exam consists of questions that will test the candidate's understanding of data retrieval from various sources. Thus, applicants should be able to identify the data source and connect to it, modify the source settings, select and create various data sets, pick a storage mode, etc. In addition, the examinee should be familiar with the concept of data profiling and have to be able to identify anomalies, study data structures, and examine column properties as well as data statistics. This section also includes cleaning, transforming, and loading data. This means that the entrant must know how to resolve inconsistencies, transform column data types, combine queries, solve data import errors, and more. This topic will also include Microsoft Dataverse, the usage & creation of a PBIDS file, and data flow after the changes are incorporated.

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

Understanding functional and technical aspects of Exam DA-100: Analyzing Data with Microsoft Power BI Model the data

The following will be discussed in MICROSOFT DA-100 exam dumps:

  • Define quick measures
  • Remove unnecessary rows and columns
  • Define the appropriate level of data granularity
  • Flatten out a parent-child hierarchy
  • Use DAX to build complex measures
  • Develop a data model
  • Use CALCULATE to manipulate filters
  • Improve cardinality levels through summarization
  • Create calculated columns
  • Implement row-level security roles
  • Create semi-additive measures
  • Optimize model performance
  • Identify poorly performing measures, relationships, and visuals
  • Define a relationship's cardinality and cross-filter direction
  • Create and manage aggregations
  • Define role-playing dimensions
  • Configure table and column properties
  • Implement Time Intelligence using DAX
  • Create measures by using DAX
  • Resolve many-to-many relationships
  • Set up the Q&A feature
  • Apply cross-filter direction and security filtering
  • Define the tables
  • Use basic statistical functions to enhance data
  • Replace numeric columns with measures
  • Design a data model
  • Create hierarchies
  • Improve cardinality levels by changing data types
  • Create a common date table
  • Design the data model to meet performance requirements
  • Create calculated tables

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Microsoft DA-100 Exam Syllabus Topics:

SectionWeightObjectives
Visualize the data25-30%- Enhance reports
  • 1. Apply filters, slicers, and drill-through
    • 2. Use bookmarks and navigation elements
      - Create reports
      • 1. Design interactive reports in Power BI Desktop
        • 2. Use visuals such as charts, maps, and tables
          Prepare the data20-25%- Clean and transform data
          • 1. Use Power Query Editor for data shaping
            • 2. Handle missing, duplicate, and inconsistent data
              - Get data from data sources
              • 1. Connect to data sources (files, databases, cloud services)
                • 2. Select appropriate storage modes (Import, DirectQuery, Composite)
                  Deploy and maintain deliverables10-15%- Manage workspaces and datasets
                  • 1. Configure dataset refresh schedules
                    • 2. Publish reports to Power BI Service
                      - Secure and share content
                      • 1. Implement row-level security (RLS)
                        • 2. Share dashboards with users and groups
                          Model the data25-30%- Create calculated columns and measures
                          • 1. Optimize model performance
                            • 2. Use DAX for calculations
                              - Design data models
                              • 1. Create relationships between tables
                                • 2. Design star and snowflake schemas
                                  Analyze the data10-15%- Perform advanced analytics
                                  • 1. Apply statistical measures in reports
                                    • 2. Use DAX time intelligence functions
                                      - Analyze data patterns
                                      • 1. Identify trends and outliers
                                        • 2. Use AI visuals and quick insights
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