MB-260: Microsoft Customer Data Platform Specialty

• Describe Dynamics 365 Customer Insights – Data (5–10%)

• Ingest data (10–15%)

• Create customer profiles through data unification (35–40%)

• Implement AI predictions (5–10%)

• Configure measures and segments (15–20%)

• Configure third-party connections (5–10%)

• Administer Customer Insights – Data (5–10%)

• 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 APIs

• Describe the integration between Customers Insights - Data and Customer Insights - Journeys

• Describe use cases for machine learning

• 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

• 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

• Select Customer Insights tables and attributes for unification

• Describe attribute types

• Describe the requirements for a primary key

• Deduplicate enriched tables

• Define deduplication rules, including exceptions, winner, and alternate records

• Manage merged preferences

• 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

• 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

• Describe business unit separation prerequisites

• Access business data in Dataverse

• Implement Customer Insights - Data business unit integrations

• Review and create customer profiles

• View the results of data unification

• Verify output tables from data unification

• Update the unification settings

• 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

• Define which fields should be searchable

• Define filter options for fields

• Define indexed fields

• 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

• 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

• Create and manage tags

• Describe the different types of measures

• Create a measure

• Configure measure calculations

• Modify dimensions

• Schedule measures

• 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

• Describe how the system suggests segments for use

• Create a suggested segment based on a measure

• Create a suggested segment based on activity

• Configure overlap segments

• Configure differentiated segments

• Review the overlap or differentiator analysis

• Find similar customers by using AI

• 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

• Enrich customer profiles

• Configure and manage enrichments

• Enrich data sources before unification

• 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

• Differentiate between system refreshes and data source refreshes

• Describe the system refresh process

• Configure a system refresh schedule

• Monitor and troubleshoot refreshes

4 Days