Numerous　studies　have　been　devoted　to　the　estimation　and　inference　problems　for　functional　linear　models　(FLM).　However,　few　works　focus　on　model　checking　problem　that　ensures　the　reliability　of　results.　Limited　tests　in　this　area　do　not　have　tractable　null　distributions　or　asymptotic　analysis　under　alternatives.　Also,　the　functional　predictor　is　usually　assumed　to　be　fully　observed,　which　is　impractical.　To　address　these　problems,　we　propose　an　adaptive　model　checking　test　for　FLM.　It　combines　regular　moment-based　and　conditional　moment-based　tests,　and　achieves　model　adaptivity　via　the　dimension　of　a　residual-based　subspace.　The　advantages　of　our　test　are　manifold.　First,　it　has　a　tractable　chi-squared　null　distribution　and　higher　powers　under　the　alternatives　than　its　components.　Second,　asymptotic　properties　under　different　underlying　models　are　developed,　including　the　unvisited　local　alternatives.　Third,　the　test　statistic　is　constructed　upon　finite　grid　points,　which　incorporates　the　discrete　nature　of　collected　data.　We　develop　the　desirable　relationship　between　sample　size　and　number　of　grid　points　to　maintain　the　asymptotic　properties.　Besides,　we　provide　a　data-driven　approach　to　estimate　the　dimension　leading　to　model　adaptivity,　which　is　promising　in　sufficient　dimension　reduction.　We　conduct　comprehensive　numerical　experiments　to　demonstrate　the　advantages　the　test　inherits　from　its　two　simple　components.