Microsoft Dynamics 365 Customer Insights (Data) Specialist

 

• Candidates for this exam implement solutions that provide insights into customer profiles and that track engagement activities to help improve customer experiences and increase customer retention

• Candidates should have firsthand experience with Dynamics 365 Customer Insights and one or more additional Dynamics 365 apps, Microsoft Power Query, Microsoft Dataverse, Common Data Model, and Microsoft Power Platform. They should also have direct experience with
practices related to privacy, compliance, consent, security, responsible AI, and data retention policy.
• Candidates need experience with processes related to KPIs, data retention, validation, visualization, preparation, matching, fragmentation, segmentation, and enhancement. They should have a general understanding of Azure Machine Learning, Azure Synapse Analytics, and Azure Data Factory.

Design Customer Insights solutions (5–10%) Describe Customer Insights

• describe Customer Insights components, including entities, relationships, activities, measures, and segments
• analyze Customer Insights data by using Azure Synapse Analytics
• describe support for near real-time updates
• describe support for enrichment

Describe use cases for Customer Insights

• describe use cases for Customer Insights
• describe use cases for creating reports by using Customer Insights
• describe use cases for extending Customer Insights by using Microsoft Power Platform components
• describe use cases for Customer Insights APIs

Ingest data into Customer Insights (15–20%) Connect to data sources

• determine which data sources to use
• determine whether to use the managed data lake or an organization’s data lake
• connect to Microsoft Dataverse
• connect to Common Data Model folders
• connect to data sources by using Power Query connectors
• ingest data from Azure Synapse Analytics
• ingest data by using Azure Data Factory pipelines
• describe real-time ingestion capabilities and limitations
• describe benefits of pre-unification data enrichment

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
• identify capabilities and limitations for scheduled refreshes
• configure scheduled refreshes and on-demand refreshes
• trigger refreshes by using Power Automate or the Customer Insights API

Create customer profiles by unifying data (20–25%) Implement mapping

• select Customer Insights entities and attributes for matching
• select attribute types
• select the primary key

Implement matching

• specify a match order for entities
• define match rules
• define custom match rules
• include enriched entities
• configure normalization options
• differentiate between low, medium, high, exact, and custom precision methods
• configure deduplication
• run a match process and review results

Implement merges

• 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
• group profiles
• configure customer ID generation
• run a merge and review results

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
• manage activities

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

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

• describe the different types of measures
• create a measure
• create a measure by using a template
• configure measure calculations
• modify dimensions

Create segments

• describe methods for creating segments, including blank segments
• create a segment from customer profiles, measures, or AI predictions
• find similar customers

Find suggested segments

• describe how the system suggests segments for use
• create a segment from a suggestion
• configure refreshes for suggestions

Create segment insights

• configure overlap segments
• configure differentiated segments
• analyze insights

Configure third-party connections (15–20%) 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
• 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

Implement Consent Management

• describe the capabilities of Consent Management
• import and manage consent data
• manage settings and users
• use consent data

Administer Customer Insights (5–10%) Create and configure environments

• identify who can create environments
• differentiate trial and production environments
• manage existing environments
• describe available user permissions
• configure user permissions and guest 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