Adobe Real-Time CDP Technical Practitioner AD7-E601 Dumps in PDF

Free Adobe AD7-E601 Real Questions (page: 8)

A company collects online transaction data from two different sources and onboards into AER The data is housed in two systems:
- Digital Order Line (DOL), which gets data at a daily batch latency - Streaming (Web/App)
The company prefers to use Streaming data over DOL.
Which method should the engineer use?

  1. Identity stitching
  2. Timestamp ordered
  3. Dataset precedence

Answer(s): C

Explanation:

Dataset precedence is a feature that allows users to define how data from different datasets should be merged into a single profile based on their priority. Users can specify which dataset has higher precedence over another dataset when there is conflicting data for the same attribute. This way, the company can use Streaming data over DOL when they have online transaction data from both sources.


Reference:

: [Dataset Precedence Overview](https://experienceleague.adobe.com/docs/ex



A food company offers monthly subscription meat boxes. People who buy bacon in the past 90 days typically spend 20% more. The company wants to use the Experience Platform to remind customers to add bacon to their upcoming subscription box to increase sales. Subscription boxes cannot be changed 2 weeks before delivery.
The company has the following data sources available to be imported:
- CRM system of customers
- Order Processing System detailing orders
- Product Catalog via Magento
- Adobe Analytics
The company uses the following tools to market to customers:
- Email
- Google Display Ads
- Facebook
- Adobe Target for personalization
How should the engineer set up the segment?

  1. emailOptlnFlag = Yes AND Order Contains "Bacon* AND NextDeliveryDate > 14 days
  2. Last 3 Orders Do not Contain "Bacon" AND NextDeliveryDate > 14 days AND LastWebVisit < 7 Days
  3. emailOptlnFlag = Yes AND Last 3 Orders Do not Contain 'Bacon" AND NextDeliveryDate > 14 days
  4. Last 3 Orders Do not Contain "Bacon" AND NextDeliveryDate > 14 days

Answer(s): D

Explanation:

This is the most effective workflow to send promotional discount offers to the target audience. By onboarding CRM data into AEP, the company can leverage the profile data and identity stitching features of AEP to create a unified view of their customers. By creating Adobe Analytics source connection in AEP, the company can ingest online behavior data from web and app into AEP dataset. By building and deploying a propensity score model in data science workspace, the company can use the machine learning capabilities of AEP to predict the likelihood of customers to purchase based on their profile and behavior data. By creating a segment in AEP using the propensity score, web visits, distance from store, and purchase order data attributes, the company can use the segmentation service and UI of AEP to define and evaluate their target audience based on profile attributes and behaviors. By sharing the segment with Adobe Campaign, the company can activate their segment across multiple channels, such as email, SMS, postcard, Google Display Ads, and Facebook.


Reference:

: Onboard Data into Platform : Connect Adobe Analytics Data Sources : Data Science

Workspace Overview : Create a Segment : Share Segments with Destinations



After mapping a segment to an external destination, the user notices in the dataflow run tab that some of the identities have been excluded.
What are two things that could cause this issue? (Choose two.)

  1. Some identities were not recognized by the destination
  2. Some identities were missing attributes
  3. Some identities were not enabled for activation
  4. Some identities were not meeting consent policies

Answer(s): A,D

Explanation:

These are two possible reasons why some identities have been excluded after mapping a segment to an external destination. Some identities were not recognized by the destination means that there was no matching identifier between AEP and the destination system. For example, if AEP sends email addresses as identifiers but the destination system expects phone numbers as identifiers, then there will be no match and those identities will be excluded. Some identities were not meeting consent policies means that there was a violation of data usage governance rules based on data labels and marketing actions. For example, if AEP sends PII data as part of a segment but the destination system is not authorized to use PII data for personalization purposes, then those identities will be excluded.


Reference:

: Identity Mapping Overview : Data Usage Governance Overview



A global retailer wants to send promotional discount offers to the following:
Existing customers who are:
- Within 50 miles of their nearest store
- Have made one of the following:
- Visits to the website more than 3 times in the last 2 weeks - Purchase of items worth more than $200 in the last 2 months - Have high propensity to purchase
This offer will be sent via SMS if the customer has subscribed to SMS. Otherwise, the offer will be sent via email.
They have made the following digital investment:
- Adobe Experience Platform
- Adobe Audience Manager
- Adobe Analytics
- Adobe Campaign
- Adobe Target
- Adobe Advertising Cloud
- CRM
Which steps should the retailer take to meet the requirement?

  1. 1. Onboard CRM data into AEP
    2. Create Adobe Analytics and AAM source connection in AEP
    3. Create segment in Adobe Target for visitors with high propensity score to purchase
    4. Import segment in AEP and combine with web visits, distance from store, and purchase order data attributes
    5. Share segment with Adobe Advertising Cloud
  2. 1. Onboard CRM data into AEP
    2. Create Adobe Analytics source connection In AEP
    3. Share AEP data with AAM
    4. Use Look-Alike Modeling in AAM to create the segment
    5. Share segment with Target and Campaign
  3. 1. Onboard CRM data into AEP
    2. Create segment in Adobe Target for visitors with high propensity score to purchase
    3. Import segment in AEP and combine with web visits, distance from store, and purchase order data attributes
    4. Share segment across Adobe Experience Cloud
  4. 1. Onboard CRM data into AEP
    2. Create Adobe Analytics source connection In AEP
    3. Build and deploy a propensity score model in data science workspace
    4. Create segment in AEP using the propensity score, web visits, distance from store, and purchase order data attributes
    5. Share segment with Adobe Campaign

Answer(s): C

Explanation:

This is the most suitable workflow to send personalized offers to existing customers who are within 50 miles of their nearest store, have made one of the following: visits to the website more than 3 times in the last 2 weeks, purchase of items worth more than $200 in the last 2 months, or have high propensity to purchase. By onboarding CRM data into AEP, the company can leverage the profile data and identity stitching features of AEP to create a unified view of their customers. By creating a segment in Adobe Target for visitors with high propensity score to purchase, the company can use the personalization capabilities of Adobe Target to predict the likelihood of customers to purchase based on their profile and behavior dat a. By importing the segment in AEP and combining it with web visits, distance from store, and purchase order data attributes, the company can use the segmentation service and UI of AEP to define and evaluate their target audience based on profile attributes and behaviors. By sharing the segment across Adobe Experience Cloud, the company can activate their segment across multiple channels, such as email, Google Display Ads, Facebook, and personalization.


Reference:

: Onboard Data into Platform : Create a Segment in Adobe Target : Create a Segment : Share Segments with Destinations



A clothing company is based in Canada. A marketer at the company wants to launch a summer sale email campaign to target customers who have browsed for any summer clothing in the last 90 days. The marketer wants to send out the campaign on August 1.
The company sells the following summer products:
- Sandals
- Swimwear
- T-shirts
- Shorts

The available attributes are: customer_hobbies email opt in The available events are:
producLbrowse product_category itemjd
How should the marketer configure this campaign?

  1. For attributes select: email opt in = YES
    For events select:
    product.browse = last 90 days producLcategory = sandals, swimwear, tshlrts. shorts Save the segment and schedule the export for

    For attributes select: email opt in = YES
  2. For attributes select: email opt in = YES
    For events select:
    product.browse = last 90 days product.category = Sandals, Swimwear, Tshirts, Shorts Save the segment and schedule the export for
    For attributes select: email opt in = NO
  3. For events select:
    product.browse = last 90 days product.category = Sandals. Swimwear, Tshirts, Shorts Save the segment and schedule the export for
  4. For events select:
    item_id = 1221
    product_category = Sandals, Swimwear, Tshirts, Shorts Save the segment and schedule the export for

Answer(s): B

Explanation:

This is the most appropriate workflow to launch a summer sale email campaign to target customers who have browsed for any summer clothing in the last 90 days. By selecting email opt in = YES as an attribute, the company can filter out customers who have not consented to receive email communications. By selecting product.browse = last 90 days and product.category = Sandals, Swimwear, Tshirts, Shorts as events, the company can capture customers who have shown interest in summer products recently. By saving the segment and scheduling the export for 7/31, the company can ensure that the segment is up to date and ready to be sent out on August 1.


Reference:

: Create a Segment : Schedule Segment Export



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