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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 Duration: | 150 minutes |
| Exam Price: | US$459 (member) / US$599 (non-member) |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 90 |
| Related Certifications: | FCCA CIA CISA CPA ACCA |
| Exam Format: | Multiple-choice questions, Computer-based testing, Scenario-based questions |
| Available Languages: | Spanish, English, Chinese |
| Recommended Training: | AAIA Review Manual AAIA Official Review Course |
| 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 # 250
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?
Answer: A
Explanation:
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIATM Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors--such as competitors or bots--from submitting biased or harmful data. This control protects model training and outputs from being manipulated."
NEW QUESTION # 251
When auditing the transparency of an AI system, which of the following would be the MOST effective way to understand the model's decision-making process?
Answer: C
Explanation:
Transparency in AI systems is a key requirement to ensure trust, accountability, and ethical compliance.
According to the ISACA AAIA™ Study Guide under the "AI Governance and Risk Management" section, understanding the decision-making process of an AI system falls under the principle of explainability.
Explainability refers to the degree to which an observer can understand the internal mechanics of an AI system and the rationale behind its outputs.
"Reviewing the explainability of AI outputs allows auditors and stakeholders to determine whether model decisions are interpretable and justifiable. High transparency means stakeholders can trace how and why a decision was made." While algorithm complexity and computational cost are technical considerations, they do not directly facilitate the audit of decision-making transparency. Similarly, training data diversity is essential for bias reduction but does not explain how decisions are derived. Therefore, option D is the most aligned with auditing transparency.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Transparency and Explainability"
NEW QUESTION # 252
Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?
Answer: A
NEW QUESTION # 253
An organization is reviewing its existing data governance framework after implementing an AI- based document repository solution. Which of the following should be the PRIMARY consideration?
Answer: C
Explanation:
The PRIMARY consideration is data classification (B), because the AI-based repository handles different types of documents that may contain sensitive, confidential, regulated, or public information. Proper classification ensures correct application of access controls, retention rules, encryption requirements, and privacy protections.
NEW QUESTION # 254
An organization is using a large language model (LLM) to assist in evaluating loan applications, but the training data used is known to be incomplete. Which of the following is the GREATEST associated risk?
Answer: B
Explanation:
Incomplete training dataoften leads to underrepresentation of certain applicant types, products, or scenarios.
In credit and lending, this typically translates intosystematic bias: some groups are evaluated on richer historical patterns, while others are evaluated on sparse or unrepresentative information. The greatest associated risk is thereforeunfair loan decisions(A), which can manifest as unjustified rejections, inappropriate pricing, or inconsistent risk assessments.
While delays (B), reduced satisfaction (C), or increased manual work (D) may occur, they are secondary operational issues. AAIA highlights that for financial services, the central risks includefairness, discrimination, regulatory compliance, and reputational impact. Incomplete data directly undermines fairness and can violate lending regulations and internal risk appetite.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (risk categories, including fairness and discriminatory outcomes).
ISACA AI ethics content on data completeness and representativeness in decisioning systems.
NEW QUESTION # 255
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