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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit (AAIA) Exam |
| Exam Number: | AAIA |
| Certificate Validity Period: | Not publicly specified (requires ongoing CPE maintenance after certification) |
| Real Exam Qty: | 55 |
| Exam Price: | USD 459 (member), USD 599 (non-member) |
| Passing Score: | 65% |
| Exam Format: | Multiple-choice, Closed-book, Remote proctoring or test center, Computer-based |
| Available Languages: | English |
| Related Certifications: | CISA CPA CIA |
| Exam Duration: | 120 minutes |
| Recommended Training: | ISACA AI Audit Training Resources Official AAIA Training Course Providers |
| Exam Registration: | ISACA AAIA Certification Page |
| Sample Questions: | ISACA AAIA Sample Questions |
| Exam Way: | Computer-based exam delivered via PSI test centers or remote proctoring |
| Pre Condition: | Must hold an active CISA, CIA, CPA, or equivalent ISACA-approved advanced auditing certification |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia |
ISACA AAIA 시험을 한번에 합격할수 없을가봐 두려워 하고 계시나요? 이 글을 보고 계신 분이라면 링크를 클릭하여 저희 사이트를 방문해주세요. 저희 사이트에는ISACA AAIA 시험의 가장 최신 기출문제와 예상문제를 포함하고 있는 ISACA AAIA덤프자료를 제공해드립니다.덤프에 있는 문제와 답을 완벽하게 기억하시면 가장 빠른 시일내에 가장 적은 투자로 자격증 취득이 가능합니다.
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질문 # 75
When converting data categories before training an AI model, which of the following scenarios represents the GREATEST risk?
정답：C
설명：
The AAIA™ Study Guide emphasizes that encoding categorical variables must preserve the semantic meaning and order of categories when relevant. The greatest risk occurs when ordinal data-such as customer rewards tiers-is treated as nominal through one-hot encoding, which removes the inherent order and may impair model learning.
"Improper encoding of ordinal variables as nominal can distort the model's understanding of relationships, leading to inaccurate predictions or biased outcomes." Customer reward categories (economy < business < first class) have a natural order. One-hot encoding ignores this order, potentially degrading model accuracy. Other options represent nominal data and are appropriately encoded.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Preprocessing and Feature Engineering"
질문 # 76
An organization is developing an AI system that integrates data from multiple external sources without clearly defined data ownership policies. Which of the following is the GREATEST concern in this situation?
정답：A
설명：
When integrating data from multiple external sources, unclear data ownership directly affects accountability for privacy, consent, retention, and lawful processing. This creates gaps in AI privacy compliance and accountability (option D), which is the greatest concern because violations can lead to regulatory sanctions, litigation, and serious reputational damage. AAIA's governance and risk domain emphasizes privacy and data governance programs, including clear roles, responsibilities, and ownership.
질문 # 77
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?
정답：D
설명：
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing.
According to the AAIATM Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made--false positives and false negatives--thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed--not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.
질문 # 78
Which of the following is the MOST important reason to establish AI governance structures that extend beyond regulatory compliance?
정답：B
설명：
While regulatory compliance is essential, AAIA underlines thatethical integritymust guide AI design, deployment, and monitoring. Regulations often lag behind technological capabilities; thus, relying solely on compliance leaves gaps in areas such as fairness, transparency, human dignity, and societal impact. The MOST important reason to extend governance structures beyond compliance is toensure ethical integrity throughout the AI life cycle(C) - from data collection and model design to deployment, monitoring, and retirement.
Option A focuses on privacy only, a subset of broader ethical considerations. Option B is valid but secondary; reputational protection is often a consequence of doing the right thing ethically. Option D (guardrails) is part of governance, but the overarching rationale for those guardrails is to uphold ethical principles. Therefore, comprehensive ethical stewardshipis the key driver.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (ethical AI principles, beyond compliance).
ISACA AI ethics and governance guidance emphasizing proactive, values-driven AI oversight.
질문 # 79
Which of the following metrics are the BEST indication of a mature and effective approach to an organization
' s data governance program for its AI systems?
정답：A
설명：
Documented data lineage (option B) is a cornerstone of mature data governance. The ISACA AAIA™ Study Guide highlights that "effective data governance for AI is characterized by the organization's ability to trace and document the origin, transformation, and use of data throughout its lifecycle and within AI models." This documentation provides transparency, supports accountability, and enables effective risk management.
While regular data quality audits (option C) are important, they do not, by themselves, ensure transparency or traceability. The number of projects (option A) and budget allocation (option D) are not directly indicative of governance maturity.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: " Data Governance in AI: Data Lineage and Traceability "
질문 # 80
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