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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which of the following is the BEST reason that recurrent neural networks enable language translation of documents?

Answer: B

Explanation:
Recurrent neural networks (RNNs) and their variants (such as LSTMs and GRUs) are designed to handle sequential data, capturing dependencies across time or position in a sequence. In language translation, words and phrases must be interpreted in context, where the meaning of a word depends on preceding (and, in advanced architectures, following) tokens. RNNs maintain internal state across steps, allowing the model to encode information from earlier parts of the sentence when predicting later outputs.


NEW QUESTION # 57
From an audit perspective, which of the following BEST supports the objectives of an AI governance program?

Answer: D

Explanation:
While roles (Option A), committees (Option B), and registers (Option C) are essential organizational components, the " AI Policies " themselves are the most comprehensive support for governance objectives.
Policies provide the " enforceable standards " for how AI must be built and managed. Specifically, policies addressing interpretability, transparency, and resiliency ensure that the organization's AI systems are auditable, fair, and stable. According to ISACA, the governance program must be rooted in these technical and ethical pillars to provide the necessary guardrails for AI development. Policies act as the " Source of Truth " that guides committees and stakeholders in their decision-making processes.


NEW QUESTION # 58
An IS auditor notes that an AI model achieved significantly better results on training data than on test data.
Which of the following problems with the model has the IS auditor identified?

Answer: C

Explanation:
Overfitting occurs when a model performs very well on training data but poorly on unseen data, indicating that the model has learned patterns specific to the training set rather than generalizing effectively. The AAIA™ Study Guide identifies overfitting as a common problem that impacts model reliability.
"Overfitting limits the model's applicability to real-world scenarios. It reflects excessive tailoring to the training data and poor performance on new, diverse inputs." Underfitting (A) would result in poor performance on both training and test data. Generalization (C) is the desired state, and bias (D) is a separate issue. Therefore, B is correct.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Overfitting, Underfitting, and Generalization"


NEW QUESTION # 59
Which of the following is the MOST significant benefit of performing frequent AI model testing and retraining?

Answer: B

Explanation:
AI models are not "static" systems; they are reflections of the data they were trained on. In dynamic environments, data patterns change constantly (e.g., market trends or new fraud tactics). Frequent testing and retraining ensure the model is "updated with current data," which prevents performance degradation and "data drift."


NEW QUESTION # 60
Which of the following BEST demonstrates effective coordination to ensure comprehensive oversight of an AI system deployed across multiple jurisdictions?

Answer: B

Explanation:
When AI systems span multiple jurisdictions, they are subject to different regulatory regimes, cultural expectations, and risk tolerances. AAIA emphasizes that oversight must be coordinated to avoid gaps or overlaps. Establishing joint oversight plans and communication channels between agencies (B) ensures that relevant authorities share information, align on expectations, and collectively monitor AI risks, enabling coherent and comprehensive oversight.


NEW QUESTION # 61
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