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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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
  • 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 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 (Q154-Q159):

NEW QUESTION # 154
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?

Answer: C

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 AAIA™ 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 # 155
An organization deploys an AI recruitment platform to screen job applicants. The IS auditor identifies that the platform's decisions may be influenced by model bias. Which of the following risk mitigation strategies is BEST for the auditor to recommend?

Answer: D


NEW QUESTION # 156
Which of the following data management practices poses the GREATEST risk to the reliability of an AI model ' s correlations?

Answer: C

Explanation:
While removing outliers can sometimes help with model performance, " Deleting outlier data values " arbitrarily poses the greatest risk to reliability because it can remove rare but legitimate data points that are critical for accurate pattern recognition. In domains like fraud detection or risk management, the outliers are the most important data. Distorting the natural distribution of data can lead to skewed correlations and a model that fails to recognize high-impact " tail " events. Encoding (Option B) and removing true duplicates (Option D) are standard, low-risk preprocessing steps. Correcting data types (Option C) is essential for enabling mathematical operations and is not a risk.


NEW QUESTION # 157
A retail organization uses an AI model to forecast inventory based on customer purchasing trends and updates the model quarterly. The model recently failed to recognize a surge in demand during a popular shopping season. Which of the following issues does this situation BEST demonstrate?

Answer: A


NEW QUESTION # 158
When converting data categories before training an AI model, which of the following scenarios represents the GREATEST risk?

Answer: A


NEW QUESTION # 159
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