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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 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. References: : Onboard Data into Platform : Connect Adobe Analytics Data Sources : Data Science Workspace Overview : Create a Segment : Share Segments with Destinations

This is the simplest and most efficient workflow to send the communications to the current customers who have logged into their account in the last 90 days and are from Chicago, IL. By onboarding the CRM data into AEP dataset, 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 AEP-CDP that matches the business requirements, 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 Standard, the company can activate their segment across multiple channels, such as email, SMS, and postcard. References: : Onboard Data into Platform : Create a Segment : Share Segments with Destinations

This is the most suitable criteria to meet the requirement of sending 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 selecting hasUpgraded=false as an attribute, the company can filter out customers who have already upgraded to the latest smartphone device. By selecting emailOptln=true as an attribute, the company can filter out customers who have not opted in for email communications. References: : Create a Segment

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

The correct answer is B because it matches the requirement given in the question. The engineer needs to select “Enable Hashing” to hash the email address as part of the File export. The other options do not apply hashing or are not available for destination export. References:https://experienceleague.adobe.com/docs/experience-platform/rtcdp/destinations/overview.html?lang=en

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

Schema is the structure that allows the engineer to apply usage labels to attributes. Usage labels are tags that indicate the sensitivity or classification of data, such as PII or opt-out. By applying usage labels to attributes in a schema, the engineer can enable data usage governance for datasets that use that schema. References: : Schema Overview : Data Labels Overview

The correct answer is B because it matches the requirement given in the question. The engineer must select “Enable Hashing” to hash the data according to data privacy policies when exporting any data. The other options do not apply hashing or are not available for destinationexport. References:https://experienceleague.adobe.com/docs/experience-platform/rtcdp/destinations/overview.html?lang=en

Analytical functions are functions that allow users to perform complex calculations on data within a segment definition. For example, users can use analytical functions to calculate averages, sums, counts, ranks, percentiles, and more. Analytical functions can help users create more advanced segments based on derived metrics or attributes. References: : Analytical Functions Overview