Describe Dynamics 365 Customer Insights - Data (5–10%)
Describe Customer Insights - Data functionality
Describe Customer Insights - Data components
Describe support for near real-time updates
Describe the differences between individual consumer and business account profiles
Describe support for Microsoft Fabric
Describe the tables and relationships in Customer Insights - Data
Describe real-time ingestion capabilities and limitations
Describe benefits of pre-unification data enrichment
Identify when to use the managed data lake or an organization’s own data lake
Describe use cases for Customer Insights - Data
Describe use cases for Customer Insights - Data
Describe use cases for Customer Insights - Data APIs
Describe the integration between Customers Insights - Data and Customer Insights - Journeys
Describe use cases for machine learning
Ingest data (10–15%)
Connect to data sources
Attach to Microsoft Dataverse
Attach to Azure Data Lake Storage
Ingest and transform data by using Power Query
Attach to Azure Synapse Analytics
Update Unified Customer Profile fields in near real-time
Troubleshoot common ingestion errors
Attach to data stored in Delta Lake format
Configure incremental refresh
Transform, cleanse, and load data
Select tables and columns
Resolve data inconsistencies, unexpected or null values, and data quality issues
Evaluate and transform column data types
Transform data from Dataverse
Create customer profiles through data unification (35–40%)
Select source fields
Select Customer Insights tables and attributes for unification
Describe attribute types
Describe the requirements for a primary key
Remove duplicate records
Deduplicate enriched tables
Define deduplication rules, including exceptions, winner, and alternate records
Manage merged preferences
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
Configure custom match conditions
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
Configure customer ID generation
Describe B2B unification
Implement business data separation
Describe 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 and manage activities
Combine customer profiles with activity data from unknown users
Describe how to use customer consent
Describe how to use web data for personalization
Describe relationship paths
Set the B2B account relationship with contacts
Configure search and filter indexes
Define which fields should be searchable
Define filter options for fields
Define indexed fields
Implement AI predictions (5–10%)
Configure built-in 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
Configure and manage sentiment analysis
Implement machine learning models
Describe prerequisites for using custom Azure Machine Learning models in Customer Insights - Data
Create and manage workflows that consume machine learning models
Describe prerequisites for using custom models from Azure Synapse Analytics in Customer Insights - Data
Configure measures and segments (15–20%)
Create and manage measures
Create and manage tags
Describe the different types of measures
Create a measure
Configure measure calculations
Modify dimensions
Schedule measures
Create and manage segments
Describe methods for creating segments, including segment builder and quick segments
Create a segment from customer profiles or measures
Create a segment based on a prediction model
Describe projected attributes
Schedule segments
Find suggested segments
Describe how the system suggests segments for use
Create a suggested segment based on a measure
Create a suggested segment based on activity
Create segment insights
Configure overlap segments
Configure differentiated segments
Review the overlap or differentiator analysis
Find similar customers by using AI
Configure third-party connections (5–10%)
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
Implement data enrichment
Enrich customer profiles
Configure and manage enrichments
Enrich data sources before unification
Administer Customer Insights - Data (5–10%)
Create and configure environments
Identify who can create environments
Differentiate between trial, sandbox, and production environments
Connect Customer Insights - Data to Dataverse
Connect Customer Insights - Data with Azure Data Lake Storage Account
Manage environments
Assign user permissions
Create an environment in Customer Insights - Data
Manage keys in Azure key vault
Manage system refreshes
Differentiate between system refreshes and data source refreshes
Describe the system refresh process
Configure a system refresh schedule
Monitor and troubleshoot refreshes
