When roughly half of a large contact base will qualify for journey entry, pre-segmenting the population in Automation Studio via a SQL Query Activity — and landing the result in a dedicated, purpose-built Data Extension used as the entry source — is the performance-conscious, best-practice approach. This offloads the filtering computation to SQL processing rather than making Journey Builder ' s entry-evaluation engine repeatedly scan and filter a large, unfiltered source at entry time, which becomes a meaningful bottleneck as volume grows. Option A, applying a filter directly on the Data Extension Entry Source inside Journey Builder, still forces the journey engine to evaluate the filter against the full underlying population on each entry-evaluation cycle rather than working from an already-narrowed set, making it less efficient at scale. Option B ' s Contact Data Entry Source is suited to smaller, attribute-driven contact segments and doesn ' t scale as predictably for large filtered cohorts as a pre-built query result. Option C ' s Import Activity is meant for ingesting external files, not for filtering existing internal data — a Query Activity is the correct tool for that. The ' query first, then feed a clean Data Extension into the journey ' pattern is the standard exam answer for filtering at meaningful scale. Reference: Journey Builder Entry Source Best Practices, Automation Studio Query Activity.