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Isaca AAIA Exam - Topic 3 Question 17 Discussion

Actual exam question for Isaca's AAIA exam
Question #: 17
Topic #: 3
[All AAIA Questions]

When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?

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Suggested Answer: D

In predictive maintenance use cases---such as detecting turbine failure---the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIA Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified.

''Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives.''

Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate.


Contribute your Thoughts:

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Harris
17 days ago
I practiced a similar question, and I think Accuracy might not be the best choice if the classes are imbalanced.
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Isreal
22 days ago
I'm not entirely sure, but I remember something about Precision being important in cases where false positives matter.
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Herman
27 days ago
I think we should focus on Recall since we want to catch as many failures as possible, right?
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