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About Data Analyst Associate and Who It Is Intended for

The Microsoft Certified: Data Analyst Associate is earned by passing DA-100 and is a role-based intermediate-level certification that falls under the Microsoft Power Platform certification hierarchy and authenticates the data analyst's expertise in utilizing Microsoft Power BI in maximizing the businesses' data assets value. The earner of this certificate will be competent to design and develop scalable data models along with cleaning and transformation of data to provide business values through data visualizations. Overall, this certification will be beneficial for data analysts in broadening their technological prowess and the values that they provide for businesses.

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

2. Data Model (25-30%):

The main domains of this section are:

  • Model performance optimization: This module will measure your competence in enhancing cardinality levels through changing data types and summarization. It will also evaluate your skills in eliminating unnecessary columns and rows, creating and managing aggregations, and identifying poorly performing visuals, measures, and relationships.
  • Creation of measures with DAX: The applicants should be able to utilize DAX in building multifaceted measures, use CALCULATE to maneuver filters, and utilize the fundamental statistical functions for enhancing data. This domain also requires their skills in creating semi-additive measures and implementing Time Intelligence with DAX.
  • Data model development: This objective requires one’s ability to create hierarchies, calculated tables, and calculated columns. It also covers your skills in establishing Q&A features, implementing row-level security functions, and applying security filtering and cross-filter direction.
  • Data model design: The learners need to have competence in defining tables, role-playing dimensions, quick measures, and cross-filter direction & relationship cardinality. The topic also requires their skills in designing a data model to fulfill the performance prerequisites, creating common data tables, and solving many-to-many relationships.

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

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4. Data Analysis (10-15%):

This section requires your skills in:

  • Caring out advanced analysis: This subject area covers your skills in identifying outliers, using binnings & groupings, conducting Time Series Analysis, and applying AI Insights. The students should also have competence in using decomposition tree visuals to dissect a measure and use Key Influencers for the exploration of dimensional variances.
  • Enhancing reports to expose insights: The candidates should have the expertise in personalizing visuals, applying filters & slicers and conditional formatting. They also need to demonstrate their skills in exploring statistical summary, creating reference lines with Analytics pane, and using Q&A visuals, among others.

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