New (2026) Adobe AD0-E602 Exam Dumps [Q21-Q36]

AD0-E602 Adobe

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New (2026) Adobe AD0-E602 Exam Dumps [Q21-Q36]

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New (2026) Adobe AD0-E602 Exam Dumps

Best Way To Study For Adobe AD0-E602 Exam Brilliant AD0-E602 Exam Questions PDF

Q21. A Real-Time CDP Business Practitioner is working on a case where there is a data governance policy violation on activating a segment to email targeting destination using a data field from dataset labeled as C4.
Which two are recommended resolutions for this situation to maintain existing data governance policy? (Choose two.)

Remove or modify data usage policy

Modify segment attribute or merge policy

Remove segment from destination

Remove or modify labels of the dataset

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/data-governance/dule/dule-overview.html?lang=en#contract-labels

Q22. A retailer implements Adobe real-time CDP and is able to combine in-store purchase data with purchase data from their website using a phone number as the primary key.
Which two data segments targeted in paid campaigns will become more accurate and result in CPA uplift? (Choose two.)

In-store Purchasers

Past Purchase >30 Days

Frequent Purchasers

Desktop browsers

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 in-store 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/experience-platform/segmentation/home.html?lang=en#segmentation

Q23. A Real-time CDP Business Practitioner must enable a scheduled evaluation of an organization’s segments How should the segments be toggled on?

Add all segments to a destination

Create a scheduled segment

Add all segments to schedule

According to the Adobe documentation, to enable scheduled evaluation of segments, the practitioner must add all segments to schedule by selecting them from the Segments tab and clicking on “Add to schedule” from the action bar. Reference: https://experienceleague.adobe.com/docs/experience-platform/segmentation/ui/schedule-segments.html?lang=en#schedule-segments

Q24. A Real-Time CDP Business Practitioner wants to target audiences on Linkedln.
In order to target Linkedln profiles what three identities must be used to support this targeting?

Hashed emails, phone number, GAID

Hashed emails, IDFA, GAID

IDFA, GAID, MID

According to the Adobe documentation, Linkedln Matched Audiences is a type of destination that allows matching profiles based on hashed email addresses, phone numbers, or mobile device IDs (GAID or IDFA) with Linkedln users across various Linkedln services, such as Campaign Manager and Marketing Solutions API. This type of destination can be used to target audiences on Linkedln using these three identities. Reference: https://experienceleague.adobe.com/docs/experience-platform/destinations/catalog/linkedin-matched-audiences.html?lang=en

Q25. An outdoor retailer and Adobe Real-Time CDP customer tries to create a segment that identifies females who browsed camping supplies on its website. The retailer first selected experience events of users who have visited campaign supply product pages in the past 30 days. The initial size of the segment was 450,000 profiles. However, when the retailer AND’d the gender attribute from its loyalty system, the size of the segment dropped to only 7;000 profiles.
What is the most likely explanation for this dramatic drop in segment size?

The segment logic should include an OR statement instead of an AND.

The recency needs to be expanded to include 90 days of activity.

Women are less interested in the brand’s camping supplies than men.

The majority of browsers on the website are not authenticated.

The most likely explanation for the dramatic drop in segment size is that the majority of browsers on the website are not authenticated. This means that they do not have a loyalty account or a unique identifier that can be matched with the gender attribute from the loyalty system. Therefore, they are excluded from the segment that ANDs the gender attribute with the browsing behavior. To increase the segment size, the retailer could use other methods to identify or infer the gender of anonymous browsers, such as cookies, device IDs, or third-party data.

Q26. A Real-time CDP business practitioner needs to determine how data will be prioritized and what data will be combined to create that unified view of a customer profile.
Which feature should be used?

Merge Policies

Union Schema

Identity Graph

According to the Adobe documentation, merge policies are a feature of Real-Time CDP that can be used to determine how data will be prioritized and what data will be combined to create a unified view of a customer profile. Merge policies define the rules and logic for merging profile attributes from different data sources into a single profile. Merge policies can be either predefined or custom, and they can be applied at the dataset level or the tenant level. Reference: https://experienceleague.adobe.com/docs/experience-platform/profile/merge-policies/overview.html?lang=en#merge-policies-overview

Q27. A Real-Time CDP Business Practitioner needs to analyze a segment’s profile count. The business practitioner is interested in the number of new profiles that are part of the segment and the number of profiles that are getting removed from the segment.
Which profile status types can the practitioner use for this scenario?

Realized and Leaving

Qualified and Existing

Realized and Exiting

According to the Adobe documentation, realized profiles are those that have newly qualified for a segment, while exiting profiles are those that have been removed from a segment due to no longer meeting the segment definition. These profile status types can be used to analyze the segment’s profile count over time. Reference: https://experienceleague.adobe.com/docs/experience-platform/segmentation/ui/segment-profile-count.html?lang=en#profile-status-types

Q28. A Real-Time Business Practitioner has to export a customer’s email address and corresponding attributes to a SFTP connection. Which destination type is recommended in this scenario?

Streaming profile export

File based destinations

Streaming segment export

According to the Adobe documentation, file based destinations are destinations that allow exporting data from Adobe Experience Platform as files to an external system or service via SFTP or Amazon S3 protocols. These destinations can export profile attributes, segment memberships, or both, using various file formats and compression methods. Reference: https://experienceleague.adobe.com/docs/experience-platform/destinations/catalog/file-based.html?lang=en

Q29. An entertainment company and Adobe Real-Time CDP customer wants to message subscribers on the anniversary of their subscription date. The goal is to send an upsell offer to increase their subscription benefits by creating a segment and activating to their email service provider.
Which segmentation event recency type should be used to evaluate new users each day into this segment once users reach 365 days since the first subscription?

This year

Rolling Range

Custom date

Within (+/-)

According to the Adobe documentation, custom date is a type of segmentation event recency that allows specifying a fixed date or a relative date based on a field in the dataset schema. This type of recency can be used to evaluate new users each day into a segment based on a specific date, such as the anniversary of their subscription date. Reference: https://experienceleague.adobe.com/docs/experience-platform/segmentation/ui/segment-builder.html?lang=en#event-recency

Q30. A Real-time CDP business practitioner is working with a client who is requiring incoming data to appear in real time to build customer profiles.
Which data ingestion type should the Real-time CDP business practitioner recommend?

Source connector ingestion

Batch ingestion

Streaming ingestion

According to the Adobe documentation2, streaming ingestion is the type of ingestion that allows data to appear in real time in customer profiles. Streaming ingestion enables data to be ingested continuously from sources such as mobile applications, websites, or IoT devices. Streaming ingestion also supports identity stitching and profile merge policies to create unified customer profiles. Reference: 2 https://experienceleague.adobe.com/docs/experience-platform/ingestion/home.html?lang=en#streaming-ingestion

Q31. An Adobe Real-time CDP customer is currently experiencing a low match rate with the Google DV360 connection when sharing cookie-based website retargeting audiences due to users visiting their website from a Safari browser. This customer also has a 70% authentication rate on their website with users logging in with an email address.
What could an Adobe Real-time CDP business practitioner suggest to improve the reach of the customer’s website retargeting audiences with Google?

Only send users who have visited the website using another browser (e.g. Chrome)

Share the segment using the Google Customer Match connection using hashed email addresses as the target identity

Share the segment using the Google Customer Match connection using ECID as the target identity

According to the Adobe documentation, Google Customer Match is a type of destination that allows matching profiles based on hashed email addresses, phone numbers, or user IDs with Google users across various Google services, such as Search, YouTube, Gmail, and Display & Video 360 (DV360). This type of destination can improve the match rate for website retargeting audiences, especially for users who visit the website from browsers that block third-party cookies, such as Safari. Reference: https://experienceleague.adobe.com/docs/experience-platform/destinations/catalog/google-customer-match.html?lang=en

Q32. A retailer who is an Adobe Real-Time CDP (RTCDP) customer wants to understand the percentage split of different age groups who buy their athletic shoes to help inform which age group to reach in their next shoe marketing campaign.
Which two steps should the retailer take to analyze their shoe segment in this way using RTCDP? (Choose two.)

Create multiple segments which OR each age group attribute with athletic shoe purchaser events

Divide the size of each age group segment into the total sum of all purchaser events to get percentage splits for analysis

Divide the size of each age group segment into the total sum of athletic shoe purchaser events to get percentage splits for analysis

Create multiple segments which AND each age group attribute with athletic shoe purchaser events

Two steps that the retailer should take to analyze their shoe segment in this way using RTCDP are to divide the size of each age group segment into the total sum of athletic shoe purchaser events to get percentage splits for analysis and to create multiple segments which AND each age group attribute with athletic shoe purchaser events. The first step allows the retailer to calculate the proportion of each age group among the shoe buyers, while the second step allows the retailer to create segments that include only the shoe buyers who belong to a specific age group.

Q33. An Adobe Real-Time CDP Business Practitioner needs to exclude certain opt-out events from all segments used in off-site targeting by a company data governance policy enforcer.
Which type of data usage label should be applied to this data?

Sensitive

Contract

Identity

According to the Adobe documentation, contract is a type of data usage label that indicates data that has contractual limitations on how it can be used, such as opt-out events or third-party data. Contract data can be excluded from segments used in off-site targeting by applying data usage policies that enforce data governance rules. Reference: https://experienceleague.adobe.com/docs/experience-platform/data-governance/dule/dule-overview.html?lang=en#dule-overview

Q34. A Real-Time CDP Business Practitioner has a client who wants to target audiences across Facebook. To send segments to this social destination, what are the first two steps they should take? (Choose two.)

Check Facebook account prerequisites

Confirm use cases for journey optimization

Connect to the destination

Export segment to destination

According to the Adobe documentation, the first step to send segments to Facebook is to check the Facebook account prerequisites, such as having a Facebook Business Manager account, a Facebook app ID, and a Facebook system user access token. The second step is to connect to the destination by creating a new destination of type Facebook connection and providing the required information. Reference: https://experienceleague.adob

Q35. A marketer sets up a new Amazon S3 source in real-time CDR A few days later, the marketer wants to check for any errors with ingestion.
In which two places should the marketer look for ingestion errors? (Choose two.)

Dataset activity

Source’s dataflow activity

Segments detail

Profile details

According to the Adobe documentation1, dataset activity and source’s dataflow activity are two places where ingestion errors can be viewed. Dataset activity shows the status of each batch that is ingested into a dataset, including any errors or warnings. Source’s dataflow activity shows the status of each source connection run, including any errors or warnings. Reference: 1 https://experienceleague.adobe.com/docs/experience-platform/sources/ui/monitor.html?lang=en#viewing-ingestion-errors

Q36. A Real-time CDP Business Practitioner is working to apply data labels according to DULE labeling.
At which level would should the business practitioner apply labels to propagate to chosen datasets and fields?

Dataset

Table

Connection

Schema

According to the Adobe documentation, DULE labeling is a framework that allows applying data usage labels (DULs) to data fields based on their usage category (Descriptive, Underlying, Linked, or Extracted). DULs can be applied at the schema level, which means they will propagate to all datasets and fields that use that schema. Reference: https://experienceleague.adobe.com/docs/experience-platform/data-governance/dule/dule-overview.html?lang=en#dule-overview

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