Which four actions should you perform in sequence to meet this requirement?

Data exports and scheduled refreshes are not allowed in test environments.

Data Source

AdventureWorks Cycles uses Customer Insights to connect to data from three different sources to generate a unified customer record. The data ingestion has been done for the initial data load. There are three data sources containing customer profile data loaded to a dedicated storage account and

container in the Azure Data Lake:

Loyalty data source: This data source contains customer profile information from in-store purchases.

– loyalty.member.csv: srcid (primary key), firstname, lastname, middlename, fullname, addressstreet, loyalty_email, city, zipcode, state, homephone, datecreated, timestamp

Ecommerce Data source: This data source contains customer profile information from online purchases.

– ecom.member.csv: ecid (primary key), firstname, last name, fullname, email, homephone, streetaddress, city, zip, state, datecreated, timestamp

Cycling Clubs Data Source: This data source contains customer profile information for members of Cycling clubs.

– cclubcust.csv: ccid (primary key) firstname, lastname, full_name, email, main phone, streetaddress1, city, zip_code, state, datecreated, datecreated, timestamp

The Loyalty data source contains the largest and most trusted dataset. It is considered the Primary Source followed by Ecommerce and Cycling Clubs Data Sources.

All three data sources share common customer demographics. Map, Match, and Merge (M3) rules within audience insights are applied accordingly to generate a unified customer record.

Additionally, there are three data sources that contain customer cellphone numbers for Loyalty, Ecommerce, and Cycling Club data sources that have been loaded to the Azure Data Lake but have not been ingested into audience insights.

cellPhone_loyaly.csv: srcid (primary key), cellphone

cellPhone_ec.csv: ecid (primary key), cellphone

cellPhone_cc.csv: ccid (primary key), cellphone

Pain Points

The AdventureWorks Cycles leadership team identified several pain points that need to be addressed immediately to support current growth and ensure customer satisfaction.

✑ Lack of strategy for refreshing the customer data in the audience insights. There is a considerable effort needed to build pipelines to flow the incremental data updates into the Azure Data Lake so it can be ingested and processed in audience insights.

✑ Customer Service reps cannot search for customers efficiently in audience insights which affects the customer satisfaction. Also, they do not have valid cell phone numbers for customers since it is not part of the profile.

✑ The Sales team uses the Dynamics 365 Sales app but are not able to use segments generated in audience insights to generate marketing lists.

✑ Marketing campaigns often sound redundant and inefficient as the same

messaging is being sent to multiple members of the same household.

✑ The Marketing team cannot create fully personalized communications due to missing Full Name in the unified customer record.

✑ The test team is complaining that they do not have a dedicated UAT environment where they can test features before they are deployed to production.

Project Goals

✑ Create a strategy to implement incremental data refresh in prod audience insights that reads data from Azure Data Lake Gen 2. In parallel configure incremental refresh in one of the non-production audience insights where all the data sources are available, loaded from Azure SQL database, through Power Query to audience insights instance. This will allow some testing of the incremental refresh functionality to be completed while the long-term strategy is being finalized.

✑ Implement necessary changes to address the remaining pain points identified during the Leadership Team meeting.

Detailed Requirements

Pain Points

✑ Configuring incremental refreshes for all customer data profiles as follows:

– Incremental data refresh should be configured for member tables only

– Timestamp data and time field should be used by the system to check when the record was last updated

– All three tables should be refreshed every two days

✑ Adding additional data sources and search fields to audience insights

– Ingesting Cell phone data- the requirement is to keep the name of the date sources aligned with the design document. See section 1 for more details.

– Furthermore, to get a quick snapshot of the quality of data, data profiling should be enabled for the phone fields only

– The following fields from the unified customer record should be added to index: Last Name, Full Name, Email, Cell Phone, Street Address, DOB

✑ Ability to use segments from the audience insights to generate marketing lists

– The Sales team needs to generate a marketing campaign based on segment of customers who have a Loyalty email. (loyalty.email)

✑ Ability to group customer profiles into a household cluster for purpose of generating targeted marketing communication

– A household cluster is defined as customers who share Last Name, Street Address, City, Zip Code and State

✑ Adding Full Name field to the unified customer record

– Full Name is a merged field with the following merging policy

a) loyalty.member.fullname

b) ecom.member.fullname

c) cclubcust.csv.full_name

✑ Creating a sandbox environment that mirrors the current development environment

– Create a sandbox environment called UAT1 and copy configurations from env. “DEV1”. a. Note: there is also an exiting environment called “Dev” and it is not configured correctly and should not be copied

DRAG DROP

You are a Customer Data Platform Specialist. You are implementing an incremental refresh in audience insights. All the data is stored in an Azure SQL database and is ingested to audience insights using Power Query. You need to configure an incremental refresh for data sources.

Which four actions should you perform in sequence to meet this requirement? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.