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| Topic | Details |
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| 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.
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| Topic 2 | - 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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| 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 (Q15-Q20):
NEW QUESTION # 15
A bank is deploying an AI model trained on customer transactions in a multi-cloud environment.
Which of the following BEST supports regulatory compliance regarding data security and privacy?
- A. Data governance framework
- B. Data protection impact assessment (DPIA)
- C. Data residency and sovereignty controls
- D. Privacy by design
Answer: C
Explanation:
In a multi-cloud environment, data often traverses various geographical regions. For financial institutions, "Data residency and sovereignty" are the most critical compliance factors, ensuring that data is stored and processed within the jurisdictions required by law (e.g., GDPR or local banking acts). Failure to control where data resides can lead to severe legal penalties. While
"Privacy by Design" and "DPIA" are essential methodologies, they do not specifically address the physical and legal location of data across cloud providers. Sovereignty controls ensure the bank maintains control over its data assets and complies with specific cross-border data transfer regulations.
NEW QUESTION # 16
An IS auditor is reviewing a dataset used by a university. Which of the following MOST likely indicates a risk that the model could not process all data and make necessary correlations?
- A. Grade Level field in float format
- B. Student Number field in integer format
- C. Having an undergraduate degree in Boolean format
- D. Final Grade Percent field in object format
Answer: D
Explanation:
In machine learning, "Data Typing" is foundational. If a numerical field (like Grade Percent) is stored as an "Object" (string/text), the AI model cannot perform mathematical operations on it, such as calculating correlations or averages. According to the AAIATM manual, this is a "Data Quality" failure that leads to "Feature Exclusion," where the model simply ignores the variable or treats each number as a unique text label, losing all quantitative meaning. The other formats (Integer, Float, Boolean) are appropriate for their respective data types.
NEW QUESTION # 17
A healthcare AI tool recommends treatments with high success rates but significant risk. The hospital prioritizes patient safety over innovation. What is the BEST course of action?
- A. Adjust the AI's parameters to align with the hospital's risk tolerance.
- B. Obtain patients' consent for the use of their data by the AI tool.
- C. Use the AI tool only for low-risk situations.
- D. Discontinue using the AI tool and rely solely on doctor expertise.
Answer: A
Explanation:
AI systems must align with the organization's risk appetite and ethical principles, especially in healthcare where patient safety is paramount. The BEST action is to adjust the AI's parameters (A) so the recommendations reflect the hospital's conservative risk tolerance, reducing the frequency of high-risk suggestions. AAIA stresses AI governance alignment with organizational risk appetite, treatment guidelines, and ethical priorities.
NEW QUESTION # 18
An IS auditor is interviewing management about implemented controls around machine learning (ML) models deployed in the production environment. Which of the following schedules for reviewing the performance of a deployed model would be of GREATEST concern to the auditor?
- A. After changes to hardware and software platforms
- B. One time prior to migrating to production
- C. After functionality changes
- D. On an annual recurring basis
Answer: B
NEW QUESTION # 19
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?
- A. It accelerates AI implementation timelines by fully automating data preparation processes.
- B. It reduces the need for human oversight, ensuring seamless and autonomous data governance.
- C. It fosters adherence to industry regulations while minimizing the risk of data breaches and privacy violations.
- D. It focuses on enhancing the accuracy and reliability of AI model predictions.
Answer: C
NEW QUESTION # 20
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