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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 (Q241-Q246):

NEW QUESTION # 241
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training data. Which of the following is the auditor's BEST recommendation to address this issue?

A. Implement a manual review process to ensure no copyrighted images are used in generated outputs.
B. Suspend the use of the platform until the training data is sanitized.
C. Use a platform that certifies the provenance and licensing of its training data.
D. Label all AI-generated images to disclaim the possibility of third-party content.

Answer: C

Explanation:
Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIATM Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.
"Organizations must verify the provenance and licensing of data used to train AI systems.
Platforms that certify data sources reduce the risk of using protected intellectual property without consent."

NEW QUESTION # 242
An organization is developing an AI system that integrates data from multiple external sources without clearly defined data ownership policies. Which of the following is the GREATEST concern in this situation?

A. Excessive dependence on automated data collection and cleansing
B. Limited documentation of user access permissions
C. Gaps in AI privacy compliance and accountability
D. Deficiencies in policies and procedures validating AI model accuracy

Answer: C

Explanation:
When integrating data from multiple external sources, uncleardata ownershipdirectly affects accountability for privacy, consent, retention, and lawful processing. This createsgaps in AI privacy compliance and accountability(option D), which is the greatest concern because violations can lead to regulatory sanctions, litigation, and serious reputational damage. AAIA's governance and risk domain emphasizesprivacy and data governance programs, including clear roles, responsibilities, and ownership.
Option A is important but relates more to accuracy and performance validation, not the foundational legal and accountability issues. Option B (access permissions) is a control issue but usually easier to remediate once ownership and responsibilities are clarified. Option C (dependence on automated data collection/cleansing) can introduce quality risks but does not directly resolve or define who is accountable. Thus, the primary risk is the lack of clear privacy and accountability structures across external data sources.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Privacy and Data Governance Programs (data governance, privacy considerations).
ISACA guidance on AI governance, roles, responsibilities, and accountability for data use.

NEW QUESTION # 243
When using off-the-shelf AI models, which of the following is the MOST appropriate way for organizations to approach vendor management?

A. Use the vendor only if the contract has been reviewed by the information security department.
B. Ensure a minimum of three quotes have been obtained for market research and comparison.
C. Only use models from vendors with globally recognized accreditation.
D. Establish responsibility and clear terms for model updates and support.

Answer: D

NEW QUESTION # 244
Which of the following is MOST important for an IS auditor to consider when collecting data for analysis by AI tools?

A. Model weights used for AI training
B. Data classification categories
C. Location of and access restrictions to the data
D. Data format and syntax requirements

Answer: D

Explanation:
When collecting data for analysis by AI tools in an audit, the MOST important immediate consideration is that the data format and syntax align with the requirements of the AI tools (C). Without correct formatting (e.g., structured fields, correct data types, consistent delimiters, proper encoding), AI tools may fail, misinterpret data, or generate unreliable results. AAIA's domain on AI in audit processes stresses data preparation, cleansing, and transformation as critical steps before applying AI analytics.
Data classification (A) and access restrictions (B) are significant from a security and privacy standpoint, but for the AI analysis itself to function correctly, the format/syntax is foundational. Model weights (D) relate to the AI training phase, not to the auditor's data collection step. Thus, ensuring the data provided to AI audit tools is properly structured and syntactically compatible is the primary technical concern.
References:
ISACA, AAIA Exam Content Outline - Domain 3: AI in Audit Processes (data preparation for AI-based audit analytics).
ISACA guidance on data quality, structure, and formatting for AI-supported audit procedures.

NEW QUESTION # 245
A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?

A. Asking the same questions later when the algorithm has changed after further learning
B. Presenting theoretical situations to justify the reason for asking the questions
C. Randomly placing keywords unrelated to the main topic
D. Submitting the same questions in a foreign language translated by another AI-based system

Answer: B

Explanation:
Prompt engineering manipulation often involves disguising inappropriate questions in hypothetical or conditional formats, tricking the AI system into bypassing its filters. The AAIA™ Study Guide highlights this as a common attack vector in generative AI misuse.
"Adversaries may reframe queries as academic or theoretical scenarios to exploit AI models' reasoning patterns, bypassing ethical constraints. Auditors should test for these circumvention techniques." Other methods (A, C, D) may work under specific conditions, but B is the most widely effective and documented prompt-engineering technique.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Prompt Engineering and Generative AI Risks"

NEW QUESTION # 246
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