You are developing a “flower” ML model… Which of the following describes an objection that...

The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related tosimilarity between the original task and the new task. Section1.8 – Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies,inherited shortcomings, andexplainability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .

However,output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classifythe same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences—not output labels. Therefore, OptionDdescribes an objection that can be safelyneglected, because output classes are the same and do not hinder reuse.