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Guarantee your MB-260 exam success with examkiller's study guide. The MB-260 practice test questions are developed by experiences Microsoft Certification Professionals who working in todays prospering companies and Microsoft exam data center.

Exam Number: MB-260

Exam Title: Microsoft Customer Data Platform Specialist

Passing Score: 700 (Total Score: 1000)(Tips: You should pass 70% for each section of the exam (bar on the chart), or else you still faild the exam even your total score more than 700 )

Origin Provider: ExamKiller

Total Questions: 50 QAs

Type: Real Exam Questions

Guarantee: 100% Pass Guarantee

Demo: Click Here for Check Demo

Microsoft MB-260 Exam Objectives

Design Customer Insights solutions (5–10%)

Describe Customer Insights

• 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 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

Ingest data into Customer Insights (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 and update data in real-time

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
• Configure incremental refresh
• Identify capabilities and limitations for scheduled refreshes
• Configure scheduled refreshes and on-demand refreshes

Create customer profiles through data unification (25–30%)

Select source fields

• Select Customer Insights entities and attributes for unification
• Select attribute types
• Select the primary key

Remove duplicate records

• Deduplicate enriched entities
• Define deduplication rules
• Review deduplication results

Match conditions

• 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

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
• Configure customer ID generation
• Group profiles into Clusters

Review data unification

• Review and create customer profiles
• View the results of data unification
• Verify output entities from data unification
• Update the unification settings

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
• Create activities in real-time
• Manage activities
• Combine customer profiles with activity data from unknown users

Implement AI predictions in Customer Insights (5–10%)

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
• Implement workflows that consume machine learning models
• Manage workflows for custom machine learning models

Configure measures and segments (15–20%)

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

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 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
• 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

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

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
• Use Consent Data

Administer Customer Insights (5–10%)

Create and configure environments

• 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

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

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