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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 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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q10-Q15):

NEW QUESTION # 10
An organization utilizes an off-the-shelf generative AI solution for internal business processing. According to the principle of shared responsibility, which of the following areas remains the PRIMARY responsibility of the organization?

Answer: C

Explanation:
In a " Software as a Service " (SaaS) or off-the-shelf AI model deployment, the " Shared Responsibility Model " dictates that the vendor is responsible for the " Security OF the model " (infrastructure, base training, core safety), while the customer is responsible for the " Security IN the model " (usage). The organization ' s primary duty is " Defining usage policies and enforcing IAM " to ensure that only authorized employees use the tool and that they do not input sensitive corporate data into unvetted prompts. This governance ensures the AI is used ethically and securely within the organization's specific operational context.


NEW QUESTION # 11
Which of the following is the GREATEST challenge when auditing an AI-based decision-making system?

Answer: A

Explanation:
The "Black Box" nature of many modern AI systems, particularly deep neural networks, makes it difficult to understand exactly how a decision was derived. "Understanding the transparency (and explainability) of AI models" is identified by the AAIATM manual as the greatest audit challenge because it limits the auditor's ability to verify fairness, detect hidden bias, or confirm compliance with logic-based regulations.


NEW QUESTION # 12
During a risk assessment for an AI system, data drift was identified as a key risk. Which of the following is the BEST course of action?

Answer: D

Explanation:
Data drift occurs when the statistical properties of input data change over time, causing the AI model to produce increasingly inaccurate or biased results.
The correct response is to document the risk and implement continuous monitoring (A) because:
Drift requires ongoing detection, not a one-time fix
Retraining too early can reinforce issues (especially with the same dataset)


NEW QUESTION # 13
From a data appropriateness and bias perspective, which of the following should be of GREATEST concern when reviewing an AI model used in a credit scoring system?

Answer: D

Explanation:
Using postal codes as a primary factor in credit scoring raises concerns of geographic and socioeconomic bias. Postal codes can serve as proxies for race, ethnicity, or income level-- potentially violating fair lending laws and ethical guidelines.
"Auditors should flag models that rely on proxies for protected attributes. Postal codes are high- risk features that may inadvertently lead to redlining or other discriminatory practices."


NEW QUESTION # 14
Which of the following is the PRIMARY purpose of an AI acceptable use policy?

Answer: A


NEW QUESTION # 15
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