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    MB-260: Microsoft Customer Data Platform Specialist (beta)

    Candidates for this exam implement solutions that provide insights into customer profiles and that track engagement activities to help improve customer experiences and increase customer retention.

    Candidates should have firsthand experience with Dynamics 365 Customer Insights and one or more additional Dynamics 365 apps, Power Query, Microsoft Dataverse, Common Data Model, and Microsoft Power Platform. They should also have direct experience with practices related to privacy, compliance, consent, security, responsible AI, and data retention policy.

    Candidates need experience with processes related to KPIs, data retention, validation, visualization, preparation, matching, fragmentation, segmentation, and enhancement. They should have a general understanding of Azure Machine Learning, Azure Synapse Analytics, and Azure Data Factory.

    Course Outline

    Describe Customer Insights

    •  describe audience insights components, including entities, relationships, activities,
      measures, and segments
    • analyze Customer Insights data by using Azure Synapse Analytics
    • describe the process for consuming engagement insights data in audience insights
    • describe support for near real-time updates
    • describe support for enrichment

    Describe use cases for Customer Insights

    • describe use cases for audience insights
    • differentiate between audience insights and engagement insights
    • describe use cases for creating reports by using Customer Insights
    • describe use cases for extending Customer Insights by using Microsoft Power Platform
      components
    • describe use cases for Customer Insights APIs

    Connect to data sources

    •  determine which data sources to use
    • determine whether to use the managed data lake or an organization’s data lake
    • connect to Microsoft Dataverse
    • connect to Common Data Model folders
    • ingest data from Azure Synapse Analytics
    • ingest data by using Azure Data Factory pipelines

    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
    • apply data shape transformations to tables

    Configure incremental refreshes for data sources

    • identify data sources that support incremental updates
    • identify capabilities and limitations for scheduled refreshes
    • configure scheduled refreshes and on-demand refreshes
    • trigger refreshes by using Power Automate or the Customer Insights API

    Implement mapping

    • select Customer Insights entities and attributes for matching
    • select attribute types

    Implement matching

    • specify a match order for entities
    • define match rules
    • configure normalization options
    • differentiate between low, medium, high, exact, and custom precision methods
    • configure deduplication
    • run a match process and review results

    Implement merges

    • specify the order of fields for merged tables
    • combine fields into a merged field
    • separate fields from a merged field
    • exclude fields from a merge
    • run a merge and review results

    Configure search and filter indexes

    • define which fields should be searchable
    • define filter options for fields
    • define indexes

    Configure relationships and activities

    • create and manage relationships
    • create activities by using a new or existing relationship
    • manage activities

    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

    Impute missing values by using predictions

    • describe processes for predicting missing values
    • implement the missing values feature

    Implement machine learning models

    • describe prerequisites for using custom Azure Machine Learning models in Customer
      Insights
    • implement workflows that consume machine learning models
    • manage workflows for custom machine learning models

    Create and manage measures

    • describe the different types of measures
    • create a measure
    • create a measure by using a template
    • configure measure calculations
    • modify dimensions

    Create segments

    • describe methods for creating segments, including blank segments
    • create a segment from customer profiles, measures, or AI predictions
    • find similar customers

    Find suggested segments

    • describe how the system suggests segments for use
    • create a segment from a suggestion
    • configure refreshes for suggestions

    Create segment insights

    • configure overlap segments
    • configure differentiated segments
    • analyze insights

    Configure connections and exports

    • configure a connection for exporting data
    • create a data export
    • schedule a data export

    Export data to Dynamics 365 Marketing or Dynamics 365 Sales

    • identify prerequisites for exporting data from Customer Insights
    • create connections between Customer Insights and Dynamics 365 apps
    • define which segments to export
    • export a Customer Insights segment into Dynamics 365 Marketing as a marketing
      segment
    • export a Customer Insights profile into Dynamics 365 Marketing for customer journey
      orchestration
    • export a Customer Insights segment into Dynamics 365 Sales as a marketing list

    Display Customer Insights data from within Dynamics 365 apps

    • identify Customer Insights data that 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
    • Create and configure environments
      identify who can create environments
    • differentiate trial and production environments
    • manage existing environments
    • describe available roles
    • configure user permissions and guest user permissions

    Manage system refreshes

    • differentiate between system refreshes and data source refreshes
    • describe refresh policies
    • configure a system refresh schedule
    • monitor and troubleshoot refreshes

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