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Exam Code: MB-260
Exam Name: Microsoft Customer Data Platform Specialist
Exam Q&As: 93 Q&As
Last update: June 19, 2024

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Introduce Microsoft MB-260 Exam

The Microsoft Customer Data Platform Specialist MB-260 exam is the part of the requirements for Microsoft Certified: Customer Data Platform Specialty certification. Candidates for the MB-260 exam implement solutions that provide insights into customer profiles and that track engagement activities to help improve customer experiences and increase customer retention. The Microsoft MB-260 exam is available in multiple languages, including English, Japanese, Chinese (Simplified), German, French, Spanish, Portuguese (Brazil), Italian. Make sure that you can pass the MB-260 exam and earn the Microsoft Certified: Customer Data Platform Specialty certification to promote your positions.

Learning MB-260 Exam Objectives Is Strongly Recommended

  • Describe Customer Insights components, including entities, relationships, activities, measures, and segments
  • Describe support for near real-time updates
  • Describe support for enrichment
  • Describe the differences between individual profiles or business accounts.
  • Describe use cases for Customer Insights
  • Describe use cases for extending Customer Insights by using Microsoft Power Platform components
  • Describe use cases for Customer Insights APIs
  • Describe use cases for working with business accounts
  • 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 and update data in real-time
  • 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
  • Identify data sources that support incremental updates
  • Configure incremental refresh
  • Identify capabilities and limitations for scheduled refreshes
  • Configure scheduled refreshes and on-demand refreshes
  • Select Customer Insights entities and attributes for unification
  • Select attribute types
  • Select the primary key
  • Deduplicate enriched entities
  • Define deduplication rules
  • Review deduplication results
  • Specify a match order for entities
  • Define match rules
  • Define exceptions
  • Include enriched entities in matching
  • Configure normalization options
  • Differentiate between basic and custom precision methods
  • 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
  • Configure customer ID generation
  • Group profiles into Clusters
  • Review and create customer profiles
  • View the results of data unification
  • Verify output entities from data unification
  • Update the unification settings
  • Define which fields should be searchable
  • Define filter options for fields
  • Define indexes
  • 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
  • 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
  • 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 tags
  • Describe the different types of measures
  • Create a measure
  • Create a measure by using a template
  • Configure measure calculations
  • Modify dimensions
  • 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
  • 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
  • Configure overlap segments
  • Configure differentiated segments
  • Analyze insights
  • Find similar segments with 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
  • 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
  • Use Customer Insights profiles and segments with real-time marketing
  • 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
  • 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
  • Enrich customer profiles
  • Configure and manage enrichments
  • Enrich data sources before unification
  • View enrichment results
  • Add Consent Data to Customer Insights
  • Use Consent Data
  • Identify who can create environments
  • Differentiate between trial and production environments
  • Connect Customer Insights to Microsoft Dataverse
  • Connect Customer Insights with Azure Data Lake Storage Account
  • Manage existing environments
  • Change or claim ownership of the environment
  • Reset an existing environment
  • Configure user permissions
  • Describe available user permissions
  • Differentiate between system refreshes and data source refreshes
  • Describe refresh policies
  • Configure a system refresh schedule
  • Monitor and troubleshoot refreshes
  • Describe when connections are used
  • Configure and manage connections

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