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>> Valid Exam AAIA Practice <<
The goal of AAIA preparation material is to help applicants prepare for the ISACA Advanced in AI Audit certification exam by providing them with the Actual AAIA Exam Questions they need to pass the exam. This ISACA Advanced in AI Audit (AAIA) study material is in the form of practice tests and AAIA PDF that thoroughly covers the content of the test.
NEW QUESTION # 153
Which of the following is the BEST key performance indicator (KPI) to measure a model ' s performance on imbalanced datasets?
Answer: B
Explanation:
For imbalanced datasets, standard " accuracy " or " error rate " is misleading (e.g., if 99% of transactions are legitimate, a model that predicts " legitimate " every time is 99% accurate but useless for fraud detection). The
" F1 score " is the harmonic mean of Precision and Recall, providing a balanced metric that considers both false positives and false negatives. It is the industry-standard KPI for evaluating how well a model handles the minority class. While Precision and Recall are valuable individually, the F1 score provides a single, comprehensive measure of the model ' s effectiveness in skewed environments.
NEW QUESTION # 154
Which of the following is the MOST significant benefit of performing frequent AI model testing and retraining?
Answer: D
Explanation:
AI models are not " static " systems; they are reflections of the data they were trained on. In dynamic environments, data patterns change constantly (e.g., market trends or new fraud tactics). Frequent testing and retraining ensure the model is " updated with current data, " which prevents performance degradation and " data drift. " While testing can assess attacks (Option A), the primary operational reason for a recurring retraining schedule is to maintain the model ' s relevance and accuracy against the most recent real-world behaviors.
NEW QUESTION # 155
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?
Answer: C
NEW QUESTION # 156
An IS auditor identified data quality issues as a result of insufficient availability of minority data to address diverse class representation. Which of the following is the BEST recommendation to resolve this issue?
Answer: C
Explanation:
When a dataset lacks sufficient examples of a " minority class " (e.g., rare diseases or specific demographic groups), the model will likely develop a bias toward the majority class. The BEST technical recommendation is to " Augment the data through synthesizing " (using techniques like SMOTE or GANs). Synthetic data generation creates artificial but statistically realistic minority samples, allowing the model to learn the characteristics of those groups without requiring more real-world data, which may be impossible to obtain.
Relabeling (Option D) does not solve the underlying scarcity of the data needed for diverse representation.
NEW QUESTION # 157
Which of the following is the BEST recommendation to mitigate excessive agency when implementing an AI system as a browser extension?
Answer: B
Explanation:
Excessive agencyoccurs when AI systems act too autonomously, making decisions without appropriate human oversight. To mitigate this in a browser extension, the BEST approach is tominimize functionality (A), ensuring the extension performs only narrowly defined, controlled actions. This aligns with AAIA's guidance onlimiting AI autonomy, applying least-privilege principles, and restricting unnecessary actions.
Removing user access entirely (B) is overly restrictive. Maximizing functionality (C) increases risk. Open- source extensions (D) improve transparency but do not inherently control excessive agency.Scope limitation is the most targeted and effective mitigation.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Governance, AI Autonomy, and Risk Controls.
NEW QUESTION # 158
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