Design Dynamics 365 Customer Insights – Data solutions (5–10%)
Describe Customer Insights – Data
Describe Dynamics 365 Customer Insights – Data components, including tables, relationships, enrichments, activities, measures, and segments

Describe the first run experience (FRE) in D365 Customer Insights – Data

Describe support for near real-time updates

Describe support for enrichment

Describe the differences between individual consumer and business account profiles.

Describe use cases for Customer Insights – Data
Describe use cases for Dynamics 365 Customer Insights – Data

Describe use cases for extending Customer Insights – Data by using Microsoft Power Platform components

Describe use cases for Customer Insights – Data APIs

Describe use cases for working with business accounts

Ingest data into Customer Insights – Data (10–15%)
Connect to data sources
Determine which data sources to use

Determine whether to use the managed data lake or an organization’s data lake

Attach to a Microsoft Dataverse data lake

Attach to Azure Data Lake Storage

Ingest and transform data using Power Query connectors

Attach to Azure Synapse Analytics

Describe real-time ingestion capabilities and limitations

Describe benefits of pre-unification data enrichment

Ingest data in real-time

Update Unified Customer Profile fields in real-time

Understand common ingestion errors

Transform, cleanse, and load data by using Power Query
Select tables and columns

Resolve data inconsistencies, unexpected or null values, and data quality issues

Evaluate and transform column data types

Configure incremental refreshes for data sources
Identify data sources that support incremental updates

Configure incremental refresh

Identify capabilities and limitations for scheduled refreshes

Configure scheduled refreshes and on-demand refreshes

Create customer profiles through data unification (30-35%)
Select source fields
Select Customer Insights tables and attributes for unification

Select attribute types

Select the primary key

Remove duplicate records
Deduplicate enriched tables

Define deduplication rules

Review deduplication results

Match conditions
Specify a match order for tables

Define match rules

Define exceptions

Include enriched tables in matching

Configure normalization options

Differentiate between basic and custom precision methods

Unify customer fields
Specify the order of fields for merged tables

Combine fields into a merged field

Combine a group of fields

Separate fields from a merged field

Exclude fields from a merge

Change the order of fields

Rename fields

Group profiles into Clusters

Implement business data separation
Understand business unit separation prerequisites

Access business data in Dataverse

Implement Customer Insights – Data business unit integrations

Review data unification
Review and create customer profiles

View the results of data unification

Verify output tables from data unification

Update the unification settings

Configure relationships and activities
Create and manage relationships

Create activities by using a new or existing relationship

Create activities in real-time

Manage activities

Combine customer profiles with activity data from unknown users

Display Customer Insights – Data Activities in D365 Activity Timeline

Create a unified contact profile for B2B accounts
Create unified contact profile

Set the relationship between contacts and accounts

Define the semantic fields

Review contact unification

Verify output tables from data unification

Configure search and filter indexes
Define which fields should be searchable

Define filter options for fields

Define indexes

Implement AI predictions in Customer Insights – Data (5–10%)
Use Copilot in Customer Insights – Data
Understand key Discovery page components
Configure prediction models
Configure and evaluate the customer churn models, including the transactional churn and subscription churn models

Configure and evaluate the product recommendation model

Configure and evaluate the customer lifetime value model

Create a customer segment based on prediction model

Configure and manage sentiment analysis

Implement machine learning models
Describe prerequisites for using custom Azure Machine Learning models in Customer Insights – Data

Use a wizard to bring custom prediction models to Customer Insights – Data

Implement workflows that consume machine learning models

Manage workflows for custom machine learning models

Configure measures and segments (10–15%)
Create and manage measures
Create and manage tags

Describe the different types of measures

Create a measure

Create a measure by using a template

Configure measure calculations

Modify dimensions

Schedule Measures

Create and manage segments
Create and manage tags

Describe methods for creating segments, including segment builder and quick segments

Create a segment from customer profiles, measures, or AI predictions

Create a segment based on a prediction model

Find similar customers

Project attributes

Track usage of segments

Export segments

Find suggested segments
Describe how the system suggests segments for use

Create a segment from a suggestion

Create a suggested segment based on activity

Configure refreshes for suggestions

Create segment insights
Configure overlap segments

Configure differentiated segments

Analyze insights

Find similar segments with AI

Configure third-party connections (10–15%)
Configure connections and exports
Configure a connection for exporting data

Create a data export

Define types of exports

Configure on demand and scheduled data exports

Define the limitations of segment exports

Export data to Dynamics 365 Customer Insights – Journeys or Dynamics 365 Sales
Identify prerequisites for exporting data from Dynamics 365 Customer Insights – Data

Create connections between Dynamics 365 Customer Insights – Data and Dynamics 365 apps

Define which segments to export

Export a Dynamics 365 Customer Insights – Data segment into Dynamics 365 Customer Insights – Journeys as a marketing segment

Use Dynamics 365 Customer Insights – Data profiles and segments with real-time marketing

Export a Dynamics 365 Customer Insights – Data profile into Dynamics 365 Customer Insights – Journeys for customer journey orchestration

Export a Dynamics 365 Customer Insights – Data segment into Dynamics 365 Sales as a marketing list

Display Customer Insights – Data data from within Dynamics 365 apps
Identify what data from Dynamics 365 Customer Insights – Data can be displayed within Dynamics 365 apps Configure the Customer Card add-in for Dynamics 365 apps

Identify permissions required to implement the Customer Card Add-in for Dynamics 365 apps

Implement Data Enrichment
Enrich customer profiles

Configure and manage enrichments

Enrich data sources before unification

View enrichment results

Use Customer Consent data
Add Consent Data to Customer Insights – Data

Use Consent Data

Use Customer Insights – Data data across Power Platform and M365 applications
Use D365 Customer Insights – Data chatbot for Microsoft Teams

Connect Power Apps and Dynamics 365 Customer Insights – Data

Use the Power Automate Connector for Dynamics 365 Customer Insights – Data

Configure the Dynamics 365 Customer Insights connector for Power BI – Data

Administer Customer Insights – Data (5–10%)
Create and configure environments
Identify who can create environments

Differentiate between trial and production environments

Connect Customer Insights – Data to Microsoft Dataverse

Connect Customer Insights – Data with Azure Data Lake Storage Account Manage existing environments

Change or claim ownership of the environment

Reset an existing environment

Delete an existing environment

Configure user permissions

Describe available user permissions

Export diagnostic logs

Manage system refreshes
Differentiate between system refreshes and data source refreshes

Describe refresh policies

Configure a system refresh schedule

Monitor and troubleshoot refreshes

Create and manage connections
Describe when connections are used

Configure and manage connections