You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?

Question

Options

• AThe possible inputs for the leaf' and flower' ML models are so different that reuse has few
• BThe probability of misclassification of the ML model "flower" is higher when it is reused than when
• CThe classification behavior of the "flower" ML model is more difficult to understand when it is
• DThe possible outputs of the "leaf" and "flower" ML models are so different that reuse has few

• A
  15% (8)
• B
  4% (2)
• C
  9% (5)
• D
  72% (39)

Explanation

The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related to similarity 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, and explain ability 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 classify the 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, Option D describes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.

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