A marketer is trying to create an audience that can be streaming evaluated, but it requires they utilize data spanning the last 7 days. The definition they have is as follows: - All profiles who have

Question

Options

• AUse data distiller to create an attribute using the batch logic and load that to profile and include in
• BUse an audience for the batch logic and include that in their audience definition with the stream-
• CUse profile computed attributes for batch logic and include that in their audience definition with the
• DUse manual batch updates to combine the batch logic and stream-based logic into one audience

• A
  7% (2)
• B
  81% (22)
• C
  7% (2)
• D
  4% (1)

Explanation

Option B works because Adobe Experience Platform supports referencing a pre-built batch audience within a streaming segment definition - the batch audience handles the complex 7-day historical logic ("started but never submitted"), while the stream segment evaluates the real-time conditions (mid-west location + active credit card browsing), enabling near-real-time qualification without sacrificing the lookback requirement. Option A (Data Distiller) is designed for SQL-based data engineering and derived dataset creation, not for building segments that feed streaming evaluation pipelines. Option C (profile computed attributes) is better suited for simple numeric aggregations like counts or sums - the exclusion-based sequential logic ("started but never submitted") is too complex to express cleanly as a computed attribute and is more naturally modeled as a full segment definition. Option D (manual batch updates) directly contradicts the goal of streaming evaluation, as it introduces a human-dependent, non-real-time refresh cycle that prevents continuous qualification.

Memory tip: Think of it as a "Batch Inside Stream" sandwich - Build your batch audience first (the filling), then wrap it in a streaming segment definition (the bread) that adds the real-time conditions on top.

No community discussion yet for this question.