Passing exam with DA-100 latest training materials
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Updated: Sep 06, 2026
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Microsoft DA-100 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Analyzing Data with Microsoft Power BI |
| Exam Number: | DA-100 |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple choice, Multiple response, Drag and drop, Build list, Case study |
| Related Certifications: | Microsoft Certified: Data Analyst Associate |
| Exam Price: | USD 165 |
| Certificate Validity Period: | Certification valid for one year, renewable |
| Passing Score: | 700 |
| Available Languages: | Japanese, Korean, Arabic (Saudi Arabia), English, Chinese (Simplified), Italian, French, Chinese (Traditional), German, Spanish, Portuguese (Brazil) |
| Real Exam Qty: | 40-60 |
| Sample Questions: | Microsoft DA-100 Sample Questions |
| Exam Way: | Online (proctored) or Onsite at a Pearson VUE testing center |
| Pre Condition: | Familiarity with data concepts and experience with data analysis |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/data-analyst-associate/ |
Understanding functional and technical aspects of Exam DA-100: Analyzing Data with Microsoft Power BI Analyze the data
The following will be discussed in MICROSOFT DA-100 exam dumps:
- Use groupings and binnings
- Apply conditional formatting
- Use the Key Influencers to explore dimensional variances
- Explore statistical summary
- Use the decomposition tree visual to break down a measure
- Create reference lines by using Analytics pane
- Identify outliers
- Enhance reports to expose insights
- Use the Q&A visual
- Apply slicers and filters
- Add a Quick Insights result to a report
- Apply AI Insights
- Personalize visuals
- Conduct Time Series analysis
- Use the Play Axis feature of a visualization
- Perform top N analysis
- Perform advanced analysis
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
Microsoft DA-100 Exam Syllabus Topics:
| Topic | Details |
|---|---|
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 |
Analyzing Data with Microsoft Power BI Exam Certification Details:
| Books / Training | DA-100T00-A: Analyzing Data with Power BI |
| Number of Questions | 40-60 |
| Sample Questions | Analyzing Data with Microsoft Power BI Sample Questions |
| Duration | 180 mins |
| Exam Price | $165 (USD) |
| Exam Code | DA-100 |
| Exam Name | Microsoft Certified - Data Analyst Associate |
| Schedule Exam | Pearson VUE |
| Passing Score | 700 / 1000 |
Useful Training Courses to Pass DA-100
To help candidates prepare fully and achieve the best possible result in DA-100, Microsoft provides an official four-day instructor-led training named ‘Course DA-100T00-A: Analyzing Data with Power BI’. It has 13 modules, each focusing on a different area that will be assessed in the exam. Along with each section, there are lab exercises that give candidates the opportunity to apply what they learnt during the sessions. After completing this training, the applicants will be able to analyze technical reports, use Power BI to make paginated reports, handle & distribute report assets, and draw up reports for data analysis. Also, they will be able to effectively enhance, polish, transform, and structure data in a way that facilitates the performance of the business.
However, not everyone can participate in this program as there are certain prerequisites. For instance, learners must be acquainted with the concepts of working with data in the cloud, know how to interact with non-relational and relational data in the cloud, be familiar with the virtualization basics, and finally, understand how to carry out data analysis. If a candidate is missing these necessary requirements, then they can obtain them by first going through the ‘Microsoft Azure Data Fundamentals’ training course and then enrolling in this one.
Moreover, note that the vendor also provides six learning paths for self-study. They are completely free and can be a good first step in your revision for the exam. These modules cover different data analysis areas, such as the use of Power BI, visualization, workspaces’ management, and many more.
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