hardMultiple Choice
AIF-C01 Building a model to predict loan default Practice Question
A company is building a model to predict loan default. They have historical data with 5% default rate. The model must minimize false negatives (missed defaults) because each default costs $50,000. False positives (incorrectly flagged defaults) cost $500 in customer service time. The model currently has a recall of 0.70 and precision of 0.80. Which of the following actions would MOST likely reduce the total cost?
⚠ Common exam trap
AWS often tests the misconception that higher precision is always better, but in cost-sensitive scenarios with asymmetric costs, maximizing recall (even at the cost of precision) is the correct strategy to minimize total financial loss.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the model's recall by lowering the classification threshold
The cost of a false negative ($50,000) is 100 times greater than a false positive ($500). Lowering the classification threshold increases recall (reduces false negatives) at the expense of precision (increases false positives). Given the extreme cost asymmetry, the net expected cost will decrease even if many more false positives occur, because each additional true positive saves $50,000 while each extra false positive costs only $500. Option D directly increases recall, which is the correct lever for this cost structure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the model's precision by raising the classification threshold
Why it's wrong here
Raising the threshold would likely decrease recall, increasing false negatives and cost.
- ✗
Use a different algorithm that trades off recall for precision
Why it's wrong here
Trading recall for precision would hurt the goal of reducing false negatives.
- ✗
Add more features to the model without changing the threshold
Why it's wrong here
Adding features may or may not improve recall; it is not a direct lever and might not reduce false negatives efficiently.
- ✓
Increase the model's recall by lowering the classification threshold
Why this is correct
Lowering the threshold captures more positives, improving recall and reducing the most costly errors (false negatives).
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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