Q: 10
A company trained an ML model on Amazon SageMaker to predict customer credit risk. The model
shows 90% recall on training data and 40% recall on unseen testing data.
Which conclusion can the company draw from these results?
Options
Discussion
A That recall gap between training and test is classic overfitting.
Seen similar recall drop questions in the official guide, that's A. Practice exams cover this classic overfitting case too.
A for sure. That huge gap in recall between train and test is typical overfitting. Doubt the issue is with data size.
A , if the test set was tiny or non-representative, maybe D, but here it's textbook overfit.
Its A, high recall on train but low on test is textbook overfitting.
A/B? If recall is high on training but way lower on test, that's usually classic overfitting (A). Underfitting (B) would show low recall on both, not just test. Unless I'm missing some context about data size, A fits better. But if training was super limited or data was weird, maybe B could pop up. Still think A is the main point here.
I don't think it's C. Big gap like 90% recall train vs 40% test usually means the model memorized training (A), not that there's not enough data. B's underfitting would show low recall across both, and D would need info about the test set being too small, which isn't in the question.
C or A? Hard to tell if they don't mention data size. Still, that recall gap usually means A.
C/D? Not really convinced by C, since a big recall drop like this is classic overfitting (A). D's possible only if test data is tiny, but nothing in the question says that. Pretty sure it's A but open to pushback.
A . That kind of recall drop screams overfitting, not a data volume issue.
Be respectful. No spam.
