The correct choices are Balanced accuracy and F1 score.

Balanced accuracy averages recall across the positive and negative classes which ensures the minority fraud class is weighted equally with the majority legitimate class and prevents the 97 to 3 majority from dominating the evaluation.

F1 score is the harmonic mean of precision and recall and it emphasizes detecting rare fraudulent transactions while also controlling false positives which helps when you need both good detection and manageable alert volume.

Overall accuracy is misleading on severely imbalanced datasets because predicting the majority class can yield a high accuracy while missing most minority positives.

Log loss measures probability calibration and penalizes confident wrong predictions but it does not directly prioritize recall for the minority class and a model can have acceptable log loss while still failing to find many fraud cases.

Mean squared error (MSE) is a regression metric and is not appropriate for evaluating binary classification performance under class imbalance.

Full AWS Practitioner Certification Question