Which of the following options is an example of the concept of overfitting?
Choose ONE option (1 out of 4)
A.
A model for predicting academic performance was trained with data from students at one university. The model shows low predictive accuracy when applied to other universities.
B.
A model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.
C.
A previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.
D.
A model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.
The ISTQB CT-AI syllabus definesoverfittingin Section3.2 – ML Model Evaluationas a condition where an ML model learns the training data too precisely—including noise and irrelevant detail—resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. OptionAperfectly matches this definition: a model trained only on one university’s student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .
Option B instead describessample biasor inadequate training diversity, not overfitting. Option C involvestransfer learningor model extension, unrelated to overfitting. Option D indicatesinsufficient training data qualityor lack of meaningful features, but not overfitting. Only Option A reflects the syllabus definition directly: overly specialized training leading to reduced predictive performance on new data.
Thus,Ais the correct and syllabus-aligned example of overfitting.
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