The correct options are Precision and Accuracy.

Permutation Feature Importance evaluates how much a chosen evaluation metric changes when a feature is randomly permuted. It therefore needs a metric that can be calculated from the model predictions on individual examples. Precision and accuracy both compare predicted labels to true labels on a per example basis, so they are appropriate metrics to use with permutation importance.

AUC ROC is incorrect because it is a ranking based summary across thresholds rather than a simple per example prediction metric. Permutation approaches typically require pointwise metrics and do not generally use ranking metrics like AUC.

Chi squared statistic is incorrect because it is a statistical test often used for assessing association or for feature selection, and it is not a direct measure of predictive performance computed from model predictions in the way accuracy or precision are.

Full AWS Practitioner Certification Question