Use this quick start guide to collect all the information about Microsoft Customer Insights Data Specialist (MB-260) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the MB-260 Microsoft Dynamics 365 Customer Insights Data Specialist exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCS Dynamics 365 - Customer Insights Data certification exam.
The Microsoft Customer Insights Data Specialist certification is mainly targeted to those candidates who want to build their career in Microsoft Dynamics 365 domain. The Microsoft Certified - Dynamics 365 Customer Insights Data Specialty exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft MCS Dynamics 365 - Customer Insights Data.
Microsoft Customer Insights Data Specialist Exam Summary:
| Exam Name | Microsoft Certified - Dynamics 365 Customer Insights Data Specialty |
| Exam Code | MB-260 |
| Exam Price | $165 (USD) |
| Duration | 120 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | MB-260T00-A: Microsoft Customer Insights - Data Specialty |
| Schedule Exam | Pearson VUE |
| Sample Questions | Microsoft Customer Insights Data Specialist Sample Questions |
| Practice Exam | Microsoft MB-260 Certification Practice Exam |
Microsoft MB-260 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Describe 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 |
Ingest 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 |
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 |
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 |
Configure 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 |
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 |
Administer 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 |
To ensure success in Microsoft MCS Dynamics 365 - Customer Insights Data certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Microsoft Dynamics 365 Customer Insights Data Specialist (MB-260) exam.