Conduct Bias Checks (e.g., Model, Data, Algorithm) PMI-CPMAI Practice Questions (Page 5)

Questions 21–22

1. 21foundation · easy

   Which of the following is an example of a bias mitigation technique applied during data preprocessing?

   • AChanging the model's loss function
   • BUsing a different optimization algorithm
   • CReweighting samples to balance group representation
   • DAdjusting the decision threshold after training
   Reveal Solution Discussion Select an answer first
2. 22application · medium

   A bank is developing a credit approval model. The training data contains 85% male applicants and 15% female applicants, and the model's overall accuracy is 92%. However, the false positive rate for female applicants is 18% compared to 6% for male applicants. The bank wants to ensure fair lending practices. What should the data science team do first?

   • ACalculate a fairness metric such as equalized odds to quantify the disparity
   • BRemove the gender attribute from the model to eliminate bias
   • CCollect more data from underrepresented groups to balance the dataset
   • DImplement a bias mitigation technique like reweighting the loss function
   Reveal Solution Discussion Select an answer first