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Analyzing Data with Microsoft Power BI (DA-100) Practice Exam

Analyzing Data with Microsoft Power BI (DA-100) Practice Exam


As a Data Analyst, your role will be to enable businesses to maximize the value of their data assets using Microsoft Power BI. Being subject matter expert, as a Data Analyst you will be responsible to perform the following tasks -

  • Designing and building scalable data models
  • Cleaning and transforming data
  • Enabling advanced analytic capabilities to provide meaningful business value using easy-to-comprehend data visualizations.

Also, as Data Analysts, you will be required to associate with key stakeholders across verticals to deliver relevant insights based on identified business requirements.


Recommended Knowledge

As a Data Analyst, it is suggested to have a fundamental understanding of data repositories and data processing both on-premises and in the cloud.


Skills Acquired

The Analyzing Data with Microsoft Power BI (DA-100) Exam has been built to measure your ability to accomplish technical tasks including - 

  • Preparing the data
  • Modelling the data
  • Visualizing the data
  • Analyzing the data
  • Deploying and maintain deliverables


Course Outline

The Analyzing Data with Microsoft Power BI (DA-100) Exam covers the following topics - 

Domain 1 - Prepare the Data (20-25%)

1.1 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 the Common Data Service (CDS)
  • use parameters

1.2 Profile the data

  • identify data anomalies
  • examine data structures
  • interrogate column properties
  • interrogate data statistics


1.3 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


Domain 2 - Model the Data (25-30%)

2.1 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


2.2 Develop a data model

  • apply cross-filter direction and security filtering
  • create calculated tables
  • create hierarchies
  • create calculated columns
  • implement row-level security roles
  • set up the Q&A feature


2.3 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


2.4 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


Domain 3 - Visualize the Data (20-25%)

3.1 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


3.2 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
  • configure data classification


3.3 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 drill through and cross filter
  • drill down into data using interactive visuals
  • export report data


4. Analyze the Data (10-15%)

4.1 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


4.2 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


5. Deploy and Maintain Deliverables (10-15%)

5.1 Manage datasets

  • configure a dataset scheduled refresh
  • configure row-level security group membership
  • providing access to datasets
  • configure incremental refresh settings
  • endorse a dataset


5.2 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


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  • Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
  • Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
  • You can also create your own practice exam based on your choice and preference 

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