Certified Tester AI Testing
Domain 5Objective 1
Confusion Matrix CT-AI Practice Questions (Page 2)
Part of the Domain 5: ML Functional Performance Metrics domain, which makes up ~5% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~1–2 in this domain), expect 1–1 from this objective — we provide 9 practice questions to prepare you well beyond it. (estimate)
9questions here
2free pages
4concepts
Questions 6–9
- 6
In a binary classification confusion matrix, which cell represents the number of negative instances that were incorrectly classified as positive?
Select an answer first - 7
A binary classifier for cancer detection was tested on a dataset of 1,000 patients. The confusion matrix shows: TP=90, FP=10, FN=20, TN=880. The clinic wants to minimize the number of healthy patients who are incorrectly told they have cancer. Which metric should be maximized?
Select an answer first - 8
A fraud detection model was tested on a balanced dataset of 1,000 transactions. The confusion matrix shows 450 true negatives, 40 false positives, 30 false negatives, and 480 true positives. The business team wants to minimize the risk of missing fraudulent transactions, even if it means more false alarms. Which metric should the team prioritize to evaluate the model's ability to catch fraud?
Select an answer first - 9
A medical diagnostic model for a rare disease was evaluated on a test set of 10,000 patients. The confusion matrix shows: TP=50, FP=100, FN=10, TN=9,840. The clinic wants to ensure that when the model says a patient has the disease, it is highly likely to be correct, because a false positive would lead to unnecessary invasive procedures. Which metric should be maximized?
Select an answer first
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