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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 (Q130-Q135):

NEW QUESTION # 130
An organization seeks to sustain effective AI governance and risk management amid rapidly evolving AI technologies. Which of the following represents the MOST effective course of action?

Answer: A

Explanation:
Sustaining effective AI governance and risk management requirescontinuous, organization-wide awareness, not just one-off or role-limited training. Option D embeds AI topics (governance, risk, ethics, privacy, and security) into the existingsecurity awareness program, which is already a recurring and mandatory mechanism across the enterprise. This supports ongoing adaptation to rapidly evolving AI technologies, aligns with ISACA's emphasis on integrating AI risk considerations into existing governance and risk frameworks, and ensures that staff at all levels understand their responsibilities.
Options A and C are too narrow in scope, as they target only technical staff or senior management; they help but do not create pervasive, sustainable governance. Option B can supplement internal training, but outsourcing alone does not ensure continuity or alignment with internal policies.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (AI governance, AI training and awareness, program metrics).
ISACA,Advanced in AI Audit Exam Candidate Guide- sections on governance, risk, and professional responsibilities.


NEW QUESTION # 131
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: B

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 # 132
In deep learning neural networks, which of the following layers is considered MOST important for an IS auditor to evaluate because it performs feature extraction to mimic human decision- making?

Answer: A

Explanation:
In the context of Neural Networks, the "Hidden Layers" are where the actual transformation and feature extraction occur. These layers sit between the input and output, processing data through weighted connections to identify complex patterns. For an IS auditor, evaluating the hidden layers is critical because they represent the "logic" of the model that emulates human-like cognition.
While input and output layers are transparent, the hidden layers often lack interpretability, leading to risks of hidden bias or non-deterministic behavior. Understanding the depth and activation functions of these layers helps auditors assess the model's complexity and its susceptibility to errors.


NEW QUESTION # 133
Which of the following BEST ensures that an AI system complies with user data ownership rights under privacy regulations?

Answer: D

Explanation:
A transparent data consent management process ensures users are informed about how their data will be used, and enables them to exercise their rights to consent, access, rectify, or delete their data. This is a core requirement under regulations such as GDPR and CCPA.
"Consent management is fundamental to respecting data ownership rights. Organizations must clearly disclose data usage purposes, provide opt-in/out capabilities, and maintain audit trails of user interactions."


NEW QUESTION # 134
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?

Answer: A

Explanation:
Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIA™ Study Guide, "problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization." If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization's mission or priorities.
Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Project Lifecycle Alignment"


NEW QUESTION # 135
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