IT Certification

Microsoft MB-260 Real Exam Questions

Last Update: 27 Sep 2023


Guarantee your MB-260 exam success with examkiller's study guide. The MB-260 practice test questions are developed by experiences Microsoft Certification Profession...


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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