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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 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.
Topic 3
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q233-Q238):

NEW QUESTION # 233
Which of the following is the BEST way to ensure an AI model's outputs are effectively controlled for bias?

Answer: A

Explanation:
The most objective evidence of bias control is "Performance Parity". If the accuracy, precision, and recall are "Similar across various demographic groups," it demonstrates that the model is functioning equitably for all users.


NEW QUESTION # 234
An organization deployed an AI-powered customer service chatbot trained using customer chat logs. During a risk assessment, which issue should be the IS auditor's GREATEST concern?

Answer: C

Explanation:
The GREATEST concern is insufficient access controls (D), which can lead to unauthorized exposure of customer data--a severe privacy, security, regulatory, and reputational risk. Chat logs often contain personally identifiable information and sensitive communications. AAIA prioritizes data confidentiality, access control, and privacy obligations as highest-risk elements, particularly for customer-interactive AI systems.


NEW QUESTION # 235
Which of the following is the BEST recommendation to mitigate excessive agency when implementing an AI system as a browser extension?

Answer: C

Explanation:
Excessive agency occurs when AI systems act too autonomously, making decisions without appropriate human oversight. To mitigate this in a browser extension, the BEST approach is to minimize functionality (A), ensuring the extension performs only narrowly defined, controlled actions. This aligns with AAIA's guidance on limiting AI autonomy, applying least-privilege principles, and restricting unnecessary actions.


NEW QUESTION # 236
An IS auditor is planning for an audit of an organization's use of AI to screen applicants for open positions. Which of the following is MOST important to include within the audit scope?

Answer: A

Explanation:
In recruitment AI, the integrity and fairness of the output are almost entirely dependent on the
"Sources of data used." If the training data comes from historical records that contain human bias (e.g., only hiring from certain universities or genders), the AI will codify and automate that bias.
The ISACA AAIATM manual states that auditing the "Data Source" and "Data Lineage" is the most effective way to identify the root cause of algorithmic discrimination.


NEW QUESTION # 237
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?

Answer: D

Explanation:
Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI AuditTM (AAIATM) Study Guide, "algorithmic transparency--enabling auditors and stakeholders to see and understand the rationale behind automated decisions--is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements." Transparent justification of decisions (option C) directly supports an auditor's ability to evaluate the appropriateness, logic, and fairness of the AI's outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.


NEW QUESTION # 238
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