Microsoft Customer Data Platform Specialist (MB-260) Practice Exam - RETIRED

Microsoft Customer Data Platform Specialist (MB-260) Practice Exam

 

The Microsoft Customer Data Platform Specialist (MB-260) certification is designed for individuals having the expertise to deploy solutions that yield insights into customer profiles and track engagement activities, aiming to:

• Enhance customer experiences.
• Boost customer retention rates.

 

Knowledge Requirement:

Candidates should have hands-on experience with:

• Dynamics 365 Customer Insights - Data and at least one additional Dynamics 365 application.
• Microsoft Power Query.
• Microsoft Dataverse.
• Common Data Model.
• Microsoft Power Platform.

Additionally, candidates should possess direct experience in:

• Privacy protocols.
• Compliance standards.
• Consent management.
• Security measures.
• Responsible AI practices.
• Data retention policies.

 

Who should take the exam?

Ideal candidates for this certification must have experience with processes about key performance indicators (KPIs), data retention, validation, visualization, preparation, matching, segmentation, and enrichment. A general understanding of:

• Azure Machine Learning.
• Azure Synapse Analytics.
• Azure Data Factory.

 

Exam Details 

• Exam Code: MB-260 
• Exam Name: Microsoft Dynamics 365 Customer Insights (Data) Specialist
• Exam Languages: English, Japanese, Chinese (Simplified), German, French, Spanish, Portuguese (Brazil), Italian
• Exam Questions: 40-60 Questions
• Passing Score: 700 or greater (On a scale 1 - 1000)

 

Exam Course Outline 

The Microsoft MB-260 Exam covers the given topics  - 

Domain 1 : Learn about Dynamics 365 Customer Insights - Data (5–10%)

Describe Customer Insights - Data functionality

• 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
• 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

 

Domain 2: Understand about Ingesting data (10–15%)

Connect to data sources

• 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

 

Transform, cleanse, and load data

• 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

 

Domain 3: Learn how to create customer profiles through data unification (35–40%)

Select source fields

• Select Customer Insights tables and attributes for unification
• Describe attribute types
• Describe the requirements for a primary key

 

Remove duplicate records

• Deduplicate enriched tables
• Define deduplication rules, including exceptions, winner, and alternate records
• Manage merged preferences

 

Match conditions

• 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

 

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
• Group profiles into Clusters
• Configure customer ID generation
• Describe B2B unification

 

Implement business data separation

• Describe business unit separation prerequisites
• Access business data in Dataverse
• Implement Customer Insights - Data business unit integrations

 

Review data unification

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

 

Configure relationships and activities

• 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

 

Configure search and filter indexes

• Define which fields should be searchable
• Define filter options for fields
• Define indexed fields

 

Domain 4: Learn how to implement AI predictions (5–10%)

Configure built-in 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
• Configure and manage sentiment analysis

 

Implement machine learning models

• 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

 

Domain 5: Understand about configuring measures and segments (15–20%)

Create and manage measures

• Create and manage tags
• Describe the different types of measures
• Create a measure
• Configure measure calculations
• Modify dimensions
• Schedule measures

 

Create and manage segments

• 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

 

Find suggested segments

• Describe how the system suggests segments for use
• Create a suggested segment based on a measure
• Create a suggested segment based on activity

 

Create segment insights

• Configure overlap segments
• Configure differentiated segments
• Review the overlap or differentiator analysis
• Find similar customers by using AI

 

Domain 6: Learn how to configure third-party connections (5–10%)

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

 

Implement data enrichment

• Enrich customer profiles
• Configure and manage enrichments
• Enrich data sources before unification

 

Domain 7: Administering Customer Insights - Data (5–10%)

Create and configure environments

• 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

 

Manage system refreshes

• Differentiate between system refreshes and data source refreshes
• Describe the system refresh process
• Configure a system refresh schedule
• Monitor and troubleshoot refreshes