Isaca ISACA Advanced in AI Risk AAIR Question # 7 Topic 1 Discussion
AAIR Exam Topic 1 Question 7 Discussion:
Question #: 7
Topic #: 1
Which of the following is the MOST important reason that risk practitioners should distinguish among traditional supervised models, unsupervised models, and large language models (LLMs) when assessing AI risk?
A.
Each type of model has different explainability, fairness, and resiliency profiles.
B.
Data used for LLMs requires more extensive cleaning and preprocessing.
C.
Parameter counts and complexity vary widely between different model types.
D.
Unsupervised models inherently introduce more hallucination risk.
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. Supervised, unsupervised, and large language models have different risk profiles for explainability, fairness, robustness, hallucination, data dependence, and validation. Distinguishing model types allows the risk assessment to select controls that match the actual technology. This makes option A, Each type of model has different explainability, fairness, and resiliency profiles, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
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