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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Passing Score: | 450 (scaled, range 200–800) |
| Exam Format: | Computer-based testing, Multiple-choice questions, Scenario-based questions |
| Exam Price: | US$459 (member) / US$599 (non-member) |
| Exam Duration: | 150 minutes |
| Available Languages: | English, Spanish, Chinese |
| Real Exam Qty: | 90 |
| Certificate Validity Period: | 3 years |
| Related Certifications: | FCCA CISA CPA CIA ACCA |
| Recommended Training: | AAIA Official Review Course AAIA Review Manual |
| Exam Registration: | ISACA Official Registration |
| Sample Questions: | ISACA AAIA Sample Questions |
| Exam Way: | Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong) |
| Pre Condition: | Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline |
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NEW QUESTION # 149
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: D
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 # 150
During an audit of an investment organization's AI-powered software, an IS auditor identifies a potential security risk. What is the GREATEST risk associated with staff exfiltrating organizational data to a generative AI tool?
Answer: B
Explanation:
The AAIA™ Study Guide stresses that inputting confidential or proprietary data into third-party generative AI tools may result in unauthorized data disclosure. These tools may store, process, or retrain on the input data, leading to privacy and intellectual property risks.
"When employees input sensitive data into external AI tools, organizations risk losing control over that information. This may result in regulatory non-compliance, legal exposure, and irreversible data leakage." While business disruption (C) and reliance (D) are notable, the most severe and immediate risk is B- unauthorized disclosure. Data contamination (A) impacts model reliability, not data security.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Privacy and Use of External AI Tools"
NEW QUESTION # 151
When initiating an AI governance program, which of the following is MOST critical to ensure the AI system aligns with organizational objectives and stakeholder needs?
Answer: B
Explanation:
Effective AI governance begins with "Problem Framing" and process identification. Before acquiring hardware (Option B) or setting up monitoring (Option C), the organization must clearly define the specific business processes the AI will augment or automate. This ensures that the development of the model is rooted in strategic objectives rather than technological hype. The ISACA AAIATM framework emphasizes that governance structures must first validate the "use case" to ensure it is ethical, feasible, and value-adding. Without a well-defined use case, the organization risks implementing AI that is legally compliant (Option A) but operationally irrelevant or harmful to its core mission.
NEW QUESTION # 152
Which metric is MOST important to consider when reviewing the performance of a machine learning model in avoiding false positive results?
Answer: C
Explanation:
Precision measures the proportion of positive predictions that were actually correct. When avoiding false positives is the priority (e.g., fraud accusations, security alerts, loan denials), precision is the critical metric because:
High precision = very few false positives
Low precision = many false positives
AAIA stresses using appropriate performance metrics depending on risk context.
NEW QUESTION # 153
To confirm the fairness of AI model decisions, the BEST way to collect reliable evidence during an AI audit is by:
Answer: C
NEW QUESTION # 154
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