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Adobe Exam AD0-E602 Topic 3 Question 44 Discussion

Actual exam question for Adobe's AD0-E602 exam
Question #: 44
Topic #: 3
[All AD0-E602 Questions]

As a Real-Time Business Practitioner, it is required to create a custom merge policy in Adobe Experience Platform for an upcoming use case to ensure profile attributes to take precedence from latest datasets and are applied across all datasets automatically.

Which merge method should the practitioner select?

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Suggested Answer: B

According to the Adobe documentation, timestamp ordered is a merge method that can be used to create a custom merge policy in Adobe Experience Platform for an upcoming use case to ensure profile attributes take precedence from latest datasets and are applied across all datasets automatically. Timestamp ordered merge method prioritizes profile attributes based on their ingestion timestamp, with newer values taking precedence over older values. Timestamp ordered merge method also applies globally across all datasets within a tenant. Reference: https://experienceleague.adobe.com/docs/experience-platform/profile/merge-policies/methods.html?lang=en#timestamp-ordered


by Jerlene at Mar 20, 2025, 06:53 AM

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Hyun
4 days ago
Wait, is this a trick question? I'm going to have to go with C) Dataset precedence. Fingers crossed I don't get merged with the wrong dataset!
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Lonna
5 days ago
I'm picturing a data nerd's dream come true - custom merge policies! C) Dataset precedence is the way to go, in my humble opinion.
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Kirk
6 days ago
Hmm, this is a tricky one. I'm going to go with C) Dataset precedence. Gotta love those custom merge policies!
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Lorriane
6 days ago
I disagree, I believe Timestamp ordered would be a better choice.
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Elvis
8 days ago
I think the practitioner should select Dataset timestamp.
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Rickie
13 days ago
B) Timestamp ordered seems more logical to me. Shouldn't the most recent data take precedence regardless of the dataset?
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Simona
14 days ago
I think the correct answer is C) Dataset precedence. It seems like the best way to ensure the latest profile attributes are applied across all datasets.
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