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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operations | 46% | - Third-Party and Supply Chain Risk - AI System Lifecycle and Deployment - Operational Controls and Readiness - Performance Monitoring and Evaluation - Incident Management and Resilience |
| Topic 2: AI Governance and Risk | 33% | - AI Governance and Program Management - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs - AI Risk Management - AI Models, Considerations, and Requirements |
| Topic 3: AI Auditing Tools and Techniques | 21% | - Audit Planning and Design - Audit Evidence Collection Techniques - Audit Testing and Sampling Methodologies - Data Quality and Analytics for AI Audit - AI Audit Outputs and Reporting |
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NEW QUESTION # 29
Which of the following data management practices poses the GREATEST risk to the reliability of an AI model ' s correlations?
Answer: D
Explanation:
While removing outliers can sometimes help with model performance, " Deleting outlier data values " arbitrarily poses the greatest risk to reliability because it can remove rare but legitimate data points that are critical for accurate pattern recognition. In domains like fraud detection or risk management, the outliers are the most important data. Distorting the natural distribution of data can lead to skewed correlations and a model that fails to recognize high-impact " tail " events. Encoding (Option B) and removing true duplicates (Option D) are standard, low-risk preprocessing steps. Correcting data types (Option C) is essential for enabling mathematical operations and is not a risk.
NEW QUESTION # 30
An AI tool is being implemented for a regional healthcare organization. Which of the following training methods BEST ensures the AI output does not reveal whether someone's personal data was used?
Answer: C
Explanation:
Differential privacyintroduces carefully calibrated noise during training or query responses so that it becomes mathematically difficult to infer whether any specific individual's record is included in the training set. For healthcare data-highly sensitive and subject to strict privacy laws-this technique directly supports privacy-by-design, reducing the risk that model outputs leak membership information or reconstruct personal records.
Option A uses real patient records directly and does not, by itself, mitigate inference risk. Option B (data augmentation) may expand the dataset but does not guarantee resistance to membership inference attacks.
Option D (transfer learning using public data) can help, but if any private data is used in fine-tuning, privacy risks remain. Differential privacy, as in option C, is the most appropriate control to ensure that outputs do not reveal whether particular personal data was used.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Privacy and Data Governance Programs; Domain 2: Data Management Specific to AI (data confidentiality, data security).
ISACA guidance on privacy-by-design and AI risk management concepts reflected in AAIA.
NEW QUESTION # 31
Which of the following should be done FIRST when developing an incident management process for AI threats?
Answer: B
Explanation:
The AAIA framework states that incident response begins with roles and responsibilities. Without clearly assigned accountability, no classification, escalation, or detection procedures can be effectively implemented.
Defining roles ensures:
Ownership of monitoring
Chain of command for incident decisions
Clear responsibility for documentation
Communication pathways
Allocation of resources for containment
NEW QUESTION # 32
Which of the following is the GREATEST risk of using AI to generate audit reports?
Answer: C
Explanation:
The greatest risk when using AI to generate audit reports is that it may misrepresent control effectiveness (B)-for example, by overstating the robustness of controls, understating deficiencies, or summarizing findings inaccurately. This undermines the reliability of the entire audit and can lead to poor management decisions, regulatory issues, and reputational damage. AAIA emphasizes that AI-generated artifacts still require professional judgment and validation to ensure they accurately reflect audit evidence and conclusions.
Inconsistent formatting (A) and lack of integration (C) are usability and efficiency issues, not fundamental assurance risks. Inability to incorporate historical findings (D) reduces context but does not inherently misstate the current control assessment. Therefore, misrepresentation of control effectiveness is the most critical risk from an assurance standpoint.
References:
ISACA, AAIA Exam Content Outline - Domain 3: AI in Audit Processes (Audit reporting and communication).
ISACA guidance on professional skepticism and validation of AI-generated insights in audit.
NEW QUESTION # 33
A retail organization uses an AI model to analyze customers' purchase history in order to offer personalized discounts. Which of the following practices represents the MOST ethical use of customer data?
Answer: D
NEW QUESTION # 34
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