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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 |
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What should you know about the Microsoft DA-100 exam before going for it?
The intended candidates for the Microsoft DA-100 exam are Data Analysts. These professionals help the organizations make the most of the value of data assets through the use of Microsoft Power BI. They also collaborate with the major stakeholders across the company to deliver the appropriate insights based on the recognized business prerequisites.
Microsoft DA-100 is an associate-level certification test, and as such, it does not have any official prerequisites. However, Microsoft recommends that the students have a good fundamental knowledge of data processing and data repositories both in Cloud and on-premises. The candidates for the exam should have the relevant skills in building and designing scalable data models, facilitating advanced analytic abilities, and transforming and cleaning data to provide significant business value through understandable data visualizations.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
Microsoft DA-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analyze the data | 10-15% | - Analyze data patterns
|
| Topic 2: Prepare the data | 20-25% | - Get data from data sources
|
| Topic 3: Visualize the data | 25-30% | - Create reports
|
| Topic 4: Deploy and maintain deliverables | 10-15% | - Secure and share content
|
| Topic 5: Model the data | 25-30% | - Design data models
|
