Analyzing Data with Microsoft Power BI Training – Exam DA-100

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Analyzing Data with Microsoft Power BI Training – Exam DA-100

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.

Microsoft Certified: Data Analyst Associate – Skills
Measured

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

Exam DA-100: Analyzing Data with Microsoft Power BI

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 join
• 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

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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