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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit
Exam Number:AAIA
Passing Score:450 (scaled, range 200–800)
Available Languages:Spanish, English, Chinese
Exam Duration:150 minutes
Real Exam Qty:90
Related Certifications:CIA
FCCA
ACCA
CISA
CPA
Certificate Validity Period:3 years
Exam Price:US$459 (member) / US$599 (non-member)
Exam Format:Computer-based testing, Scenario-based questions, Multiple-choice questions
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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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.

ISACA Advanced in AI Audit Sample Questions (Q287-Q292):

NEW QUESTION # 287
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?

Answer: D

Explanation:
When using generative AI tools during audit activities, the most critical concern is the reliability and appropriateness of the information being entered and processed. According to the AAIA™ Study Guide, auditors are accountable for ensuring that audit data is valid, confidential, and that generated outputs are factual and verifiable.
"IS auditors must evaluate whether the information entered into AI tools is reliable and appropriate for the audit context. Inputting sensitive or unverified data may lead to regulatory violations or audit inaccuracies." While hallucinations (C) and privacy notices (B) are important concerns, the primary auditor responsibility is to ensure that source data is accurate and suitable. Therefore, D is the correct response.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Auditor Responsibility and AI Input Validation"


NEW QUESTION # 288
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: C


NEW QUESTION # 289
An IS auditor reviewing the latest AI chatbot release identifies that, despite high accuracy rates, non-English users complain about the model ' s poor accuracy. Which of the following controls is BEST at ensuring detection of subgroup regressions?

Answer: D

Explanation:
A common pitfall in AI performance is " aggregate accuracy, " which masks poor performance in specific demographics or subgroups. To detect these " subgroup regressions, " auditors should look for " comparative evaluations " where the model is tested separately for different languages. " Parity thresholds " establish the maximum allowable difference in performance between the majority group (English) and minority groups.
This ensures fairness and consistent user experience. Post-go-live human review is reactive, and translating everything to English ignores the nuances of the original language that likely caused the errors in the first place.


NEW QUESTION # 290
An AI healthcare diagnostic tool requires large volumes of patient data, raising concerns about privacy and data breaches. Which of the following is the MOST effective strategy to mitigate this risk?

Answer: B

Explanation:
The most effective strategy to protect sensitive patient data is to use synthetic data or anonymized datasets for model training. This reduces exposure of personally identifiable information while allowing the model to learn meaningful medical patterns.
AAIA emphasizes privacy-by-design, de-identification, and minimal use of raw personal data in high-risk sectors such as healthcare. Anonymization and synthetic data significantly reduce the risk of re-identification or breach-related harm.
Option A (encryption) protects data in transit but does not eliminate privacy risks. Option B is impractical because healthcare models require clinically relevant datasets, not public data. Option C increases data exposure, aggravating privacy risks.
Thus, using anonymized or synthetic data is the strongest privacy protection aligned with healthcare compliance principles.
References:
AAIA Domain 5: Data Privacy, AI Ethics, and Compliance.
AAIA Domain 2: Data Management Practices for Sensitive AI Use Cases.


NEW QUESTION # 291
The BEST way to prevent sensitive information disclosure by large language model (LLM) chatbots is through:

Answer: B

Explanation:
Large Language Models (LLMs) have the capacity to memorize and unintentionally reveal sensitive information if not properly managed. As per the AAIA™ Study Guide, data masking is a critical technique that prevents the exposure of personally identifiable information (PII) or confidential content by obscuring or replacing sensitive parts of the data during training or interaction.
"Data masking ensures that training data used for LLMs does not contain real sensitive identifiers. Unlike sanitization, masking modifies data to maintain utility while eliminating exposure risk." Manual monitoring and access controls are supportive security measures, and data sanitization helps remove content but may not preserve the data's structure. Data masking offers the most proactive and technically robust solution.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Privacy and Information Protection in AI Systems"


NEW QUESTION # 292
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