Exam CT-AI Topic 10 Question 102 Discussion

Actual exam question for ISTQB's CT-AI exam
Question #: 102
Topic #: 10

In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?

A. A new application which is in the early stages of the first test cycle B. An old application with lots of defect records but a brand new development and test team C. An old application where defect records are linked to failed tests and production incidents D. An old application with few critical defect records and many non-critical defect records

Suggested Answer: C Vote an answer

The correct answer is C . The CT-AI syllabus states that ML models trained on the features of the most critical defects can be used to identify defects most likely to cause system failures that account for a large proportion of reported defects. This requires meaningful historical defect data and reliable links between defect features and real outcomes.
Option C provides the strongest training basis because defect records are linked to failed tests and production incidents. These links create useful labels and outcome evidence for learning criticality. Option A lacks historical data because the application is new and early in its first test cycle. Option B has many records, but the brand-new team reduces the relevance of people- and assignment-related patterns, and the option does not indicate that records are connected to critical outcomes. Option D is weakened by class imbalance: few critical defects and many non-critical defects make it harder for the model to learn the minority critical class.
The most effective ML setting is therefore an old application with rich, outcome-linked historical data.
References/topics: CT-AI Syllabus Chapter 11, Section 11.2 "Using AI to Analyze Reported Defects."
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by Edgar at Jun 29, 2026, 06:15 AM

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