The is_incremental() macro in dbt helps the model know if it is running incrementally or as a full refresh. When running incrementally, the macro returns True, so the model can add a filter to select only new or updated rows. This way, the model appends only new data instead of reloading everything. If the macro returns False, the model runs a full refresh and replaces all data. This process optimizes data loading and saves time. The execution table shows steps where the macro result guides the model's behavior, either filtering rows or selecting all. Variables like is_incremental() and selected_rows change accordingly during the run. Understanding this macro helps beginners write efficient dbt models.