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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.
Topic 2
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Topic 3
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.

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ISACA Advanced in AI Audit Sample Questions (Q194-Q199):

NEW QUESTION # 194
An IS auditor reviewing the latest AI chatbot release identifies that, despite high accuracy rates, non-English users complain about the model's poor accuracy. Which of the following controls is BEST at ensuring detection of subgroup regressions?

Answer: C

Explanation:
A common pitfall in AI performance is "aggregate accuracy," which masks poor performance in specific demographics or subgroups. To detect these "subgroup regressions," auditors should look for "comparative evaluations" where the model is tested separately for different languages.
"Parity thresholds" establish the maximum allowable difference in performance between the majority group (English) and minority groups. This ensures fairness and consistent user experience. Post-go-live human review is reactive, and translating everything to English ignores the nuances of the original language that likely caused the errors in the first place.


NEW QUESTION # 195
A retailer ' s AI pricing engine recommends unusually large discounts in certain regions. An audit reveals that most historical sales data used for model training came from large urban areas, while smaller regions were represented by very few or no sales records. Which of the following is the MOST likely cause of this issue?

Answer: C

Explanation:
This scenario illustrates the problem of " Data Scarcity " or " Under-representation bias. " If the model is trained primarily on urban data, it will not have enough information to understand the unique economic or competitive dynamics of rural regions. As a result, when it encounters those regions, it may make erratic or extreme recommendations (like excessive discounts) because it is essentially " guessing " based on limited data. According to the AAIA™ manual, ensuring " Data Representativeness " across all demographic or geographic subgroups is a critical step in training. Without a balanced dataset, the model fails to generalize correctly to all operational environments, leading to significant financial loss and operational instability.


NEW QUESTION # 196
A digital bank utilizes an AI system to generate credit scores. Which of the following would BEST mitigate the risk of sudden and unexplained changes in a borrower's credit score?

Answer: C

Explanation:
Sudden and unexplained changes in AI-generated credit scores may result from data drift, model overfitting, or lack of recalibration. According to the AAIATM Study Guide, regular expert review and calibration help maintain model reliability and transparency, particularly in high-stakes decisions like credit scoring.
"Ongoing human oversight ensures that predictive models remain stable and justifiable. In high- impact environments, such as banking, experts must review and recalibrate AI systems to prevent opaque or unexpected behavior."


NEW QUESTION # 197
An IS auditor notes that an AI model achieved significantly better results on training data than on test data. Which of the following problems with the model has the IS auditor identified?

Answer: D

Explanation:
Overfitting occurs when a model performs very well on training data but poorly on unseen data, indicating that the model has learned patterns specific to the training set rather than generalizing effectively. The AAIATM Study Guide identifies overfitting as a common problem that impacts model reliability.
"Overfitting limits the model's applicability to real-world scenarios. It reflects excessive tailoring to the training data and poor performance on new, diverse inputs."


NEW QUESTION # 198
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?

Answer: D


NEW QUESTION # 199
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