hardMultiple Choice
AIF-C01 Practice Question: A data science team has trained a gradient…
A data science team has trained a gradient boosting model using Amazon SageMaker to predict equipment failures. The model's confusion matrix shows 100 true negatives, 5 false positives, 20 false negatives, and 75 true positives. The cost of a false negative (missed failure) is $10,000, and the cost of a false positive (false alarm) is $500. What is the total cost of the model's predictions on this evaluation set?
⚠ Common exam trap
AWS certification exams often test the ability to correctly apply a cost matrix to a confusion matrix, where the trap is that candidates either forget to include all misclassification costs or mistakenly assign costs to correct predictions (true positives/negatives).
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
✓
$202,500
The total cost is calculated by multiplying the number of false negatives (20) by their unit cost ($10,000) and the number of false positives (5) by their unit cost ($500), then summing these values: (20 × $10,000) + (5 × $500) = $200,000 + $2,500 = $202,500. This matches option D. The true negatives and true positives have no associated cost in this evaluation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
$2,500
Why it's wrong here
$2,500 counts only the five false positives at $500 each, ignoring the twenty false negatives. The false-negative cost dominates here: 20 × $10,000 = $200,000, giving $200,000 + $2,500 = $202,500. Multiplying one error type alone tempts because the arithmetic is simple.
- ✗
$20,500
Why it's wrong here
$20,500 appears to apply $10,000 to the twenty false negatives incorrectly, or to mix error counts. The correct total is (20 × $10,000) + (5 × $500) = $200,000 + $2,500 = $202,500. Partial weighting tempts because both error types must be summed.
- ✗
$1,020,500
Why it's wrong here
The arithmetic is wrong: 20 false negatives × $10,000 = $200,000, plus 5 false positives × $500 = $2,500, giving $202,500. The figure $1,020,500 appears to treat false positives as costing $10,000 each or misreads the matrix, so it fails the stem's stated cost structure.
- ✓
$202,500
Why this is correct
Multiplying the 20 false negatives by the $10,000 missed-failure penalty gives $200,000, and the 5 false positives by the $500 false-alarm cost gives $2,500. Summing these weighted error costs yields $202,500, matching the stem's asymmetric cost constraint rather than treating all misclassifications equally.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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