A　conventional　regression　model　for　functional　data　involves　expressing　a　response　variable　in　terms　of　the　predictor　function.　Two　assumptions,　that　(i)　the　predictor　function　and　the　error　are　independent　and　(ii)　the　relationship　between　the　response　variable　and　the　predictor　function　takes　functional　linear　model,　are　usually　added　to　the　model.　Checking　the　validation　of　these　two　assumptions　is　fundamental　to　statistic　inference　and　practical　applications.　We　develop　a　test　procedure　to　check　these　assumptions　simultaneously　based　on　generalized　distance　covariance.　We　establish　the　asymptotic　theory　for　the　proposed　test　under　null　and　alternative　hypotheses,　and　provide　a　bootstrap　procedure　to　obtain　the　critical　value　of　the　test.　The　proposed　test　is　consistent　against　all　alternatives　provided　that　the　semimetrics　related　to　the　generalized　distance　are　strong　negative,　and　can　be　readily　generalized　to　other　functional　regression　models.　We　explore　the　finite　sample　performance　of　the　proposed　test　by　using　both　simulations　and　real　data　examples.　The　results　illustrate　that　the　proposed　method　has　favorable　performance　compared　with　the　competing　method.