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Synchronized Data Extensions are the native, near-real-time integration mechanism for bringing Salesforce objects — including custom fields on the standard Case object, such as the ' Contact Me ' checkbox — directly into Marketing Cloud through Marketing Cloud Connect, and critically, they support applying a filter condition (a Boolean filter on Contact Me equal to true) at setup, so only the relevant closed cases requesting follow-up are synchronized, rather than importing the full case volume and filtering afterward. This gives NTO an efficient, continuously-refreshing, pre-filtered dataset ready to drive the messaging campaign without manual, recurring import work. Option A ' s manual Report-and-Import-Wizard approach is a one-time, manually-triggered batch process lacking the ongoing, automatic refresh that Synchronized Data Extensions provide, making it poorly suited to a recurring campaign need. Option B references ' Salesforce Data Extensions ' paired with Filter Activities as if importing were a separate manual step requiring additional filtering logic downstream, which is less efficient than filtering directly at the sync configuration level. Option D incorrectly attributes data-import functionality to Journey Builder, which consumes already-available data extensions rather than performing the underlying Salesforce data import itself. Reference: Synchronized Data Extensions, Marketing Cloud Connect Filters.

The requirement explicitly calls for an attribute-to-attribute comparison between the customer number field in the journey ' s entry source data extension and the customer number field in a separate transaction data extension. A Decision Split configured for ' both Contact and Journey Data ' allows the split ' s filter criteria to reference attributes from Attribute Groups configured in Contact Builder (relational, contact-scoped data such as the transaction/redemption DE) alongside the journey ' s own entry-source attributes in a single condition — precisely the cross-DE, field-to-field comparison described. A split using only Journey Data (A) is confined to the fields present in the entry source itself and cannot reach into the separate transactional data extension. A split using only Contact Data (B) can query related, contact-linked data but loses the ability to reference journey-specific attributes in the same condition, which the scenario also implies is needed for the comparison. Einstein Split (C) is a machine-learning-driven random/predictive path allocator, not a rules-based attribute comparison tool, and is unrelated to this use case. Consultants should recognize ' compare a field in the entry source against a field in a related data extension ' as the signature use case for the combined Contact-and-Journey Data Decision Split. Reference: Journey Builder Decision Split, Data Designer Relationships, Attribute Groups.

Journey Builder performs best when the heavy lifting of segmentation happens before contacts are evaluated for entry, not inside the journey itself. Performing large-scale segmentation in Automation Studio (B) offloads CPU-intensive filtering to SQL Query Activities, which are optimized for bulk processing across millions of rows, rather than relying on Journey Builder ' s per-contact entry evaluation engine. Creating a prefiltered, dedicated sendable copy of the source data extension for each journey (D) avoids lock contention and re-evaluation overhead that occurs when multiple journeys share a single large entry source, and it ensures each journey only processes the exact population it needs. Option A is a valid best practice for decision-split performance generally, but Attribute Groups govern contact-level data joins for Contact Data-based splits and does not address entry-source scale at 10 million records specifically — it ' s a secondary optimization, not one of the two primary levers for this scenario. Option C describes filtering directly on the entry source Data Extension, which still forces Journey Builder to evaluate the full underlying population against the filter at entry time, rather than pre-segmenting it, and is less efficient than doing that work in Automation Studio beforehand. Reference: Journey Builder Performance Best Practices, Automation Studio Segmentation.

Send Logs, once enabled through Send Logging, provide a unified, cross-channel record of send activity that spans email, SMS, and push notifications, giving the consultant a single, queryable structure to build reporting that covers all three channels together rather than needing to separately reconcile disparate channel-specific data sources. This cross-channel breadth is precisely what the requirement calls for, since the scenario explicitly asks about send data spanning SMS, Push, and email together, not just one channel in isolation. The Sent Data View (B) is scoped specifically to email send activity and does not natively capture SMS or Push send events in the same structure, making it insufficient on its own for a unified, cross-channel query. Tracking Extracts (C) similarly focus primarily on email delivery and engagement tracking data and are not the standard mechanism for consolidated multi-channel (SMS/Push/email) send reporting. Contact History (D) is not a defined, standard Marketing Cloud reporting object for this purpose in this context. When a scenario explicitly spans multiple messaging channels and asks for a single queryable send-data source, Send Logs is the correct, unifying answer among the choices presented. Reference: Send Logging, Cross-Channel Reporting.