• Design Customer Insights solutions
• Ingest data into Customer Insights
• Create customer profiles by unifying data
• Implement AI predictions in Customer Insights
• Configure measures and segments
• Configure third-party connections
• Administer Customer Insights

Course Outline

Describe Customer Insights

• describe audience insights components, including entities, relationships, activities,
  measures, and segments
• analyze Customer Insights data by using Azure Synapse Analytics
• describe the process for consuming engagement insights data in audience insights
• describe support for near real-time update
• describe support for enrichment

Describe use cases for Customer Insights

• describe use cases for audience insights
• differentiate between audience insights and engagement 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

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

Implement matching

• specify a match order for entities
• define match rules
• 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
• separate fields from a merged field
• exclude fields from a merge
• run a merge and review results

Configure prediction models

• configure and evaluate the customer churn models, including the transactional churn and subscription churn model
• configure and evaluate the product recommendation model
• configure and evaluate the customer lifetime value model Impute missing values by using predictions
• describe processes for predicting missing values
• implement the missing values feature

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

Create and configure environments

• identify who can create environments
• differentiate trial and production environments
• manage existing environments
• describe available roles
• 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

About The Exam