AI fundamentals · Question 41 of 60
A team migrates a rule-based credit check to an ML model. Which consequences for testing follow from the differences between the two kinds of systems?
3 answers are correct. Hard
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Answer
- B The outputs are no longer predictable in every case, because the model works probabilistically
- D It becomes harder to understand why a particular decision was made
- E If the model keeps learning in operation, it must be continuously monitored
Explanation
Conventional systems are deterministic and therefore predictable and transparent. ML models derive their behavior from patterns in data and often work probabilistically, so outputs are not always predictable. Large models are also hard to explain, which is critical in finance in particular. Self-learning systems adapt, but they must be continuously monitored.