Microsoft DA-100 : Analyzing Data with Microsoft Power BI

DA-100 real exams

Exam Code: DA-100

Exam Name: Analyzing Data with Microsoft Power BI

Updated: Jul 21, 2026

Q & A: 150 Questions and Answers

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Exam Topics

Microsoft expects that the successful candidates get the passing score of 720 points on a scale of 100-1000. This mark is achievable if you put your mind to it. To succeed, you must understand the Microsoft DA-100 exam topics that are as follows:

1. Data Preparation (20-25%):

This part includes the following areas:

  • Cleaning, loading, & transforming data: This subject area requires that the students understand how to combine queries, utilize user-friendly value substitutions, solve data import errors, transform and measure column data types, and configure the data loading. It also covers their skills in applying data shape transformations for table structures and user-friendly conventional naming for queries and columns. The individuals should also have the expertise in leveraging Advanced Editor or modifying Power Query M-code.
  • Data profile: The test takers should have competence in interrogating data statistics and column properties as well as identifying data anomalies and evaluating data structures.
  • Collecting data from various data sources: The candidates must be able to connect and identify data sources, alternate data source settings, and choose storage mode and shared datasets, or build local datasets. They also need the capacity to select the relevant query type, utilize parameters, Microsoft Dataverse, create or use PBIDS files, and data flow.

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Certification Path for Exam DA-100: Analyzing Data with Microsoft Power BI

Analyzing Data with Microsoft Power BI is a fundamental exam and there are no pre-requisites. Successful candidates will get Microsoft Certified: Data Analyst Associate title.

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
  • Passing Score: 70%
  • Number of Questions: 40
  • Format: Multiple choices, multiple answers

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Microsoft DA-100 Exam Syllabus Topics:

TopicDetails

Prepare the Data (20-25%)

Get data from different data sources- identify and connect to a data source
- change data source settings
- select a shared dataset or create a local dataset
- select a storage mode
- choose an appropriate query type
- identify query performance issues
- use Microsoft Dataverse
- use parameters
- use or create a PBIDS file
- use or create a data flow
- connect to a dataset using the XMLA endpoint
Profile the data- identify data anomalies
- examine data structures
- interrogate column properties
- interrogate data statistics
Clean, transform, and load the data- resolve inconsistencies, unexpected or null values, and data quality issues
- apply user-friendly value replacements
- identify and create appropriate keys for joins
- evaluate and transform column data types
- apply data shape transformations to table structures
- combine queries
- apply user-friendly naming conventions to columns and queries
- leverage Advanced Editor to modify Power Query M code
- configure data loading
- resolve data import errors

Model the Data (25-30%)

Design a data model- define the tables
- configure table and column properties
- define quick measures
- flatten out a parent-child hierarchy
- define role-playing dimensions
- define a relationship's cardinality and cross-filter direction
- design the data model to meet performance requirements
- resolve many-to-many relationships
- create a common date table
- define the appropriate level of data granularity
Develop a data model- apply cross-filter direction and security filtering
- create calculated tables
- create hierarchies
- create calculated columns
- implement row-level security roles
- implement object-level security
- set up the Q&A feature
Create measures by using DAX- use DAX to build complex measures
- use CALCULATE to manipulate filters
- implement Time Intelligence using DAX
- replace numeric columns with measures
- use basic statistical functions to enhance data
- create semi-additive measures
Optimize model performance- remove unnecessary rows and columns
- identify poorly performing measures, relationships, and visuals
- improve cardinality levels by changing data types
- improve cardinality levels through summarization
- create and manage aggregations
- use Query Diagnostics

Visualize the Data (20-25%)

Create reports- add visualization items to reports
- choose an appropriate visualization type
- format and configure visualizations
- import a custom visual
- configure conditional formatting
- apply slicing and filtering
- add an R or Python visual
- configure the report page
- design and configure for accessibility
- configure automatic page refresh
- create a paginated report
Create dashboards- set mobile view
- manage tiles on a dashboard
- configure data alerts
- use the Q&A feature
- add a dashboard theme
- pin a live report page to a dashboard
Enrich reports for usability- configure bookmarks
- create custom tooltips
- edit and configure interactions between visuals
- configure navigation for a report
- apply sorting
- configure Sync Slicers
- use the selection pane
- use drillthrough and cross filter
- drilldown into data using interactive visuals
- export report data
- design reports for mobile devices

Analyze the Data (10-15%)

Enhance reports to expose insights- apply conditional formatting
- apply slicers and filters
- perform top N analysis
- explore statistical summary
- use the Q&A visual
- add a Quick Insights result to a report
- create reference lines by using Analytics pane
- use the Play Axis feature of a visualization
- personalize visuals
Perform advanced analysis- identify outliers
- conduct Time Series analysis
- use groupings and binnings
- use the Key Influencers to explore dimensional variances
- use the decomposition tree visual to break down a measure
- apply AI Insights

Deploy and Maintain Deliverables (10-15%)

Manage datasets- configure a dataset scheduled refresh
- configure row-level security group membership
- provide access to datasets
- configure incremental refresh settings
- promote or certify Power BI datasets
- identify downstream dataset dependencies
- configure large dataset format
Create and manage workspaces- create and configure a workspace
- recommend a development lifecycle strategy
- assign workspace roles
- configure and update a workspace app
- publish, import, or update assets in a workspace
- apply sensitivity labels to workspace content
- use deployment pipelines
- configure subscriptions
- promote or certify Power BI content

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

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Microsoft DA-100 Exam Syllabus Topics:

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

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