Adobe AD0-E602 Practice Test - Questions Answers, Page 3

According to the Adobe documentation, destination labels are labels that indicate the type and purpose of a destination, such as on-site or off-site, personalization or analytics, etc. Data usage policies are rules that define how data can be used based on data usage labels and destination labels. These two must be used to enforce data governance for activating segments to platform destinations by ensuring that only compatible data and destinations are allowed.

Reference: https://experienceleague.adobe.com/docs/experience-platform/datagovernance/labels/destination-labels.html?lang=en

https://experienceleague.adobe.com/docs/experience-platform/data-governance/policies/policiesoverview.html?lang=en

According to the Adobe documentation, a Data Steward is a role that is responsible for ensuring data quality, compliance, and governance across the organization. Some of the main tasks of a Data Steward include creating data policies and applying them to datasets and fields, reviewing data, datasets, and data samples to apply and manage metadata usage labeling, monitoring data quality issues and resolving them, etc. Reference: https://experienceleague.adobe.com/docs/experienceplatform/data-governance/home.html?lang=en#roles

According to the Adobe documentation, C4 is a type of contract label that indicates data that cannot be used for off-site targeting purposes, such as email targeting destination. To resolve the policy violation, the practitioner can either modify the segment attribute or merge policy to exclude the C4 data field from the segment definition, or remove the segment from the destination altogether.

Reference: https://experienceleague.adobe.com/docs/experience-platform/datagovernance/dule/dule-overview.html?lang=en#contract-labels

According to the Adobe documentation, I2 is a type of identity label that indicates data that is indirectly identifiable, such as hashed email addresses or phone numbers. These datasets can be shared per company policies, as they do not contain directly identifiable data, such as I1 or 11 labels.

Reference: https://experienceleague.adobe.com/docs/experience-platform/datagovernance/dule/dule-overview.html?lang=en#identity-labels

According to the Adobe documentation, policies and labels are two key components of the Data Governance framework within AEP. Policies are rules that define how data can be used based on data usage labels and destination labels. Labels are metadata tags that indicate the type, purpose, and usage of data and destinations. The Data Steward must consider these items during set up within AEP to ensure data quality, compliance, and governance. Reference:

https://experienceleague.adobe.com/docs/experience-platform/datagovernance/home.html?lang=en#data-governance-framework

According to the Adobe documentation, by combining in-store purchase data with online purchase data using a phone number as the primary key, the retailer can create more accurate segments of customers based on their purchase behavior across channels. For example, the retailer can target instore purchasers who have not bought online yet, or frequent purchasers who have bought both online and offline multiple times. These segments can be targeted in paid campaigns to increase conversion rates and reduce cost per acquisition (CPA). Reference:

https://experienceleague.adobe.com/docs/experienceplatform/segmentation/home.html?lang=en#segmentation

According to the Adobe documentation, suppression is a use-case that involves creating segments of existing customers who have already purchased a product or service and excluding them from paid campaigns that are aimed at acquiring new customers. This use-case could help the retailer meet their business goal of increasing ROAS by reducing wasted ad spend on existing customers and focusing on potential customers who have not bought yet. Reference:

https://experienceleague.adobe.com/docs/experience-platform/segmentation/usecases/suppression.html?lang=en

According to the Adobe documentation, lookalike modelling is a use-case that involves creating segments of high-value customers who share similar characteristics or behaviors and using them as seeds to find new prospects who are likely to have high lifetime value (LTV). This use-case could help the financial services company acquire more profitable customers by targeting them with relevant offers. Similarly, reaching the new segment with an email campaign reminding openers to set up recurring deposits is a use-case that involves creating segments of customers who have recently opened an account and encouraging them to take actions that increase their loyalty and retention.

This use-case could help the financial services company increase customer satisfaction and revenue by offering them incentives and benefits for setting up recurring deposits.

Reference: https://experienceleague.adobe.com/docs/experience-platform/segmentation/use-cases/lookalikemodeling.html?lang=en

https://experienceleague.adobe.com/docs/experienceplatform/segmentation/use-cases/customer-reengagement.html?lang=en