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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.

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ISACA Advanced in AI Audit Sample Questions (Q321-Q326):

NEW QUESTION # 321
An IS auditor learns that an organization uses AI facial recognition technology for security purposes. Which of the following is the GREATEST ethical concern with this practice?

Answer: C

Explanation:
According to the ISACA AAIA™ Study Guide, facial recognition systems are prone to significant ethical risks related to fairness and bias. These systems often exhibit higher error rates for specific demographic groups-particularly based on race, gender, and age-due to non-representative training datasets. For an IS auditor, this represents a major compliance and reputational risk, as biased outcomes can lead to discriminatory access or false accusations. While data security (Option A) and the lack of standards (Option C) are relevant, the fundamental ethical imperative in AI governance is ensuring that biometric applications do not disproportionately disadvantage protected groups.


NEW QUESTION # 322
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?

Answer: D


NEW QUESTION # 323
Which of the following would pose the GREATEST risk when reviewing AI acceptable use training content?

Answer: D

Explanation:
Thenon-deterministic nature of AIrefers to the fact that modern AI systems-especially generative AI, reinforcement learning, and probabilistic models-do not always produce the same output even when given identical inputs. According to AAIA's ethical and operational guidance, understanding non-determinism is essential for:
* Managing expectations of AI behavior
* Preventing overreliance on AI outputs
* Identifying hallucinations or inconsistent outcomes
* Ensuring human oversight remains in place
* Supporting proper audit trails and accountability
* Upholding professional skepticism toward AI-generated outputs
If training doesnotexplain non-determinism, employees may mistakenly believe AI outputs are always authoritative or correct, which can lead to unsafe reliance, ethical violations, and audit failures.
Other options (A, C, D) are helpful but not foundational. Lack of understanding of non-determinism fundamentally undermines safe and responsible AI use.
References:
AAIA Domain 5: Ethical and Legal Considerations(Explainability, Output Variability, Responsible Use).
AAIA Domain 3: Oversight and Human-in-the-Loop Principles.


NEW QUESTION # 324
An IS auditor is planning for an audit of an organization's use of AI to screen applicants for open positions. Which of the following is MOST important to include within the audit scope?

Answer: C

Explanation:
In recruitment AI, the integrity and fairness of the output are almost entirely dependent on the
"Sources of data used." If the training data comes from historical records that contain human bias (e.g., only hiring from certain universities or genders), the AI will codify and automate that bias.
The ISACA AAIATM manual states that auditing the "Data Source" and "Data Lineage" is the most effective way to identify the root cause of algorithmic discrimination.


NEW QUESTION # 325
Which of the following is the GREATEST benefit of using large language models (LLMs) to identify potential control deficiencies?

Answer: D

Explanation:
LLMs excel at processing large amounts ofunstructured text(e.g., policies, process narratives, procedures).
TheGREATEST benefitis their ability tosummarize and compare multiple documents to identify inconsistencies(B), which can signal potential control weaknesses or misalignments. This aligns with AAIA's description of AI-enabled audit analytics that supportdocument analysis, narrative comparison, and pattern detectionfor control assessment.
Option A presumes autonomy, which is unsafe without validation. Option C is more aligned with predictive or simulation models, not LLM strengths. Option D is not acceptable because walkthroughs require professional judgment and evidence gathering; AI should support, not replace. Thus,summarization and inconsistency identificationis the most practical and impactful benefit.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI in Audit Processes (use of LLMs for narrative analysis).


NEW QUESTION # 326
......

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