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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 (Q90-Q95):

NEW QUESTION # 90
Which of the following is the GREATEST benefit of using AI to evaluate data quality during an audit?

Answer: A

Explanation:
Traditional data quality checks often rely on manual sampling, which can miss rare but significant errors. AI-driven audit tools can analyze 100% of a population to "identify outliers" that represent data errors, anomalies, or potential fraud. According to the AAIATM framework, identifying these outliers is crucial because "dirty data" can fundamentally skew AI model predictions and audit conclusions. By automating the detection of data inconsistencies, auditors can focus their manual efforts on investigating high-risk items, thereby increasing both the accuracy of the audit and the overall reliability of the data used for model training.


NEW QUESTION # 91
Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?

Answer: D


NEW QUESTION # 92
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?

Answer: C


NEW QUESTION # 93
Which of the following is MOST important to consider when auditing an organization's AI procedures?

Answer: B

Explanation:
The integrity of data fed into AI systems is a critical concern. The AAIATM Study Guide emphasizes that validation and filtration processes are essential to mitigate the risk of data poisoning--an attack that can manipulate model behavior by injecting malicious inputs.
"Data poisoning represents a major vulnerability in AI pipelines. Effective controls include robust validation, filtration, and monitoring of training data sources. These preventive practices are essential to ensure model reliability and security."


NEW QUESTION # 94
An organization using AI to create digital content faces challenges in protecting its intellectual property. Which of the following is the BEST way to mitigate this risk?

Answer: D

Explanation:
For AI-generated content (images, text, or audio), "watermarking" is a critical technical control to protect Intellectual Property (IP) and ensure provenance. Digital watermarks can be embedded into the metadata or the content itself (often invisibly) to prove the organization is the creator and to track unauthorized distribution. This supports legal claims and brand protection. Firewalls and data separation protect the system, but watermarking protects the output once it is shared online or with clients. As AI makes content creation easier, the ability to prove ownership becomes an essential ethical and legal safeguard.


NEW QUESTION # 95
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