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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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This ISACA Advanced in AI Audit (AAIA) certification is a valuable credential that is designed to validate your expertise all over the world. After successfully competition of AAIA exam you can gain several personal and professional benefits. All these ISACA Advanced in AI Audit (AAIA) certification exam benefits will not only prove your skills but also assist you to put your career on the right track and achieve your career objectives in a short time period.

ISACA Advanced in AI Audit Sample Questions (Q228-Q233):

NEW QUESTION # 228
An IS auditor is reviewing AI-driven processes that automate financial approvals. Which of the following poses the GREATEST challenge to maintaining separation of duties (SOD)?

Answer: B

Explanation:
Separation of Duties (SOD) is intended to prevent errors or fraud by ensuring no single person (or entity) has control over all stages of a transaction. When AI automates financial approvals, the " Lack of transparency in the algorithm " creates a challenge because the logic that grants approval is often opaque. If the algorithm functions as a " Black Box, " it may consolidate decision-making power that was previously spread across multiple human roles, without sufficient visibility into how those decisions are made. This can lead to unauthorized or erroneous approvals going undetected. Effective AI governance must ensure that automated approvals are subject to transparent audit trails and secondary human reviews to uphold SOD principles.


NEW QUESTION # 229
Which of the following is the MOST important consideration when auditing the data used for training an AI model?

Answer: A


NEW QUESTION # 230
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?

Answer: A

Explanation:
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIA™ Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors-such as competitors or bots- from submitting biased or harmful data. This control protects model training and outputs from being manipulated." Options A and C address technical data handling but not source authenticity. Option B may inadvertently incentivize biased reviews. Option D provides a robust control against poisoning.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Input Data Security and Adversarial Risk Mitigation"


NEW QUESTION # 231
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?

Answer: B


NEW QUESTION # 232
Which of the following is MOST important to consider when evaluating ethical risk related to data used for training an AI model?

Answer: D

Explanation:
Ethical risk begins with the nature of the data being used. The sensitivity and origin of training data (option B) determine whether the model risks violating privacy, perpetuating historical bias, or using data that was collected without proper consent.
AAIA identifies data provenance, data sensitivity, and lawfulness of processing as central to ethical AI.
Training data that contains sensitive attributes (e.g., health, ethnicity, gender, financial status) must be reviewed carefully for compliance and ethical impacts.
Option A (diverse outputs) relates to performance, not ethics. Option C (update frequency) is part of lifecycle management, not ethical sourcing. Option D (cleaning methods) ensures quality but does not address ethical risk if the underlying data is inappropriate or unlawfully sourced.
Thus, sensitivity and origin of training data are the primary ethical factors.
References:
AAIA Domain 5: Ethical Principles, Fairness, Data Sensitivity Considerations.
AAIA Domain 1: Privacy and Data Governance Programs.


NEW QUESTION # 233
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