Adobe AD7-E601 Dumps

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. References: : Create a Segment : Schedule Segment Export

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. References: : Create a Segment : Schedule Segment Export

This is the most effective workflow to send win-back messaging to consumers through Instagram ads based on their last purchase, whether it occurred on the organization’s App, website, or in-store. By bringing in member profile data from Member Hub into AEP, the company can leverage the profile data and identity stitching features of AEP to create a unified view of their customers. By bringing in transaction data (order id, purchase date, purchase amount, transaction details) - From DOL, BMOL, and Clickstream into AEP dataset, the company can ingest online and offline purchase data from different sources into AEP. By creatingsegments in AEP based on channel - App, website, or in-store for customers who have made purchases in the last 2 months, 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 sending audiences to Facebook via s3 destination exports, the company can activate their segment across multiple channels, such as Instagram ads. References: : Onboard Data into Platform : Create a Segment : Share Segments with Destinations

The correct answer is A because it matches the requirement given in the question. The user needs to drag and drop email address and country attributes using ‘AND’ logic to ensure that the customers have an email address and are from the same country. Then, the user needs to create a container for painting and drawing and change the logic of that container to “OR” to ensure that the customers are interested in either painting or drawing or both. References:

The correct answer is C because it matches the scenario given in the question. The user needs to use an aggregate of transaction events to create a segment based on the total amount spent on their credit card in the last 48 hours. The other options do not allow the user to specify the time frame or the amount spent. References:

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

Governance policy is a feature that allows users to define how data can be used for different destinations based on data labels and marketing actions. Data labels are tags that indicate the sensitivity or classification of data, such as PII or opt-out. Marketing actions are categories of activities that describe how data is used for a destination, such as personalization or analytics. By creating a governance policy, users can connect policies with data labels and enable data usage enforcement for that destination. References: : Governance Policy Overview : Data Labels Overview : Marketing Actions Overview : Create a Governance Policy

The correct answer is D because it matches the scenario given in the question. The user can define time parameters for the segment in the attribute “Last_Website_Browse” or the event “Website_Browse”. The other options do not allow the user to specify the time frame of 30 days. References:

The correct answer is B because it matches the outcome of deduplication enabled on the composite key companyEmail + IName + mobilePhone. The destination file will only include one customer record per composite key, and it will choose the record with the latest data if there is a conflict. In this case, Customer 2 has a newer officePhone number than Customer 1, so Customer 2 will be included in the destination file and Customer 1 will be excluded. References:

This is the most suitable criteria to create a segment based on all customers who currently have a home loan with the credit union, pay through auto pay, and have logged in within the last 2 weeks but do not have a credit card issued through the credit union. By selecting customer exists within the client dataset as an attribute, the company can filter out non-customers from the segment. By selecting customer has logged in within the last 14 days as an attribute, the company can capture customers who have shown recent activity on their account. By selecting customer has a home loan with the credit union and customer has auto pay set up for paying the home loan as attributes, the company can identify customers who have a specific product and payment method with the credit union. By selecting customer has not acquired a credit card as an attribute, the company can exclude customers who already have a credit card issued through the credit union. References: : Create a Segment