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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 ISACA Advanced in AI Audit Exam Questions in 3 User-Friendly Formats

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ISACA Advanced in AI Audit Sample Questions (Q173-Q178):

NEW QUESTION # 173
A health organization has deployed an AI model to analyze chest X-rays. The model reports high accuracy, but thresholds are unclear and performance is not broken down by patient demographics. Why is accuracy alone insufficient to evaluate this model?

Answer: C

Explanation:
The ISACA AAIATM manual warns against "Aggregate Accuracy" in high-risk applications like healthcare. A model might be 95% accurate overall but have a 40% error rate for a specific minority demographic (e.g., based on age or ethnicity). This creates "Hidden Fairness and Safety Risks" where patients from certain groups are consistently misdiagnosed. Auditors must recommend "Stratified Evaluation," where performance metrics (Precision, Recall) are calculated separately for each subgroup to ensure equitable outcomes and patient safety.


NEW QUESTION # 174
Which of the following techniques BEST supports machine learning (ML) training in sentiment analysis?

Answer: A

Explanation:
Sentiment analysis is a classification task where text is categorized (e.g., positive, negative, or neutral).
Logistic regression is a fundamental and highly effective statistical method for binary or multi-class classification, making it a standard choice for sentiment models. It estimates the probability of an input belonging to a specific class. While more complex architectures like Transformers (LLMs) are now common, logistic regression remains a benchmark for its simplicity and transparency in audit contexts. Image recognition and ANOVA are not relevant to the linguistic processing required for sentiment analysis, and the log-rank test is typically used in survival analysis rather than NLP classification.


NEW QUESTION # 175
Which control is MOST important to verify in order to ensure proper data management with AI systems?

Answer: B

Explanation:
According to the ISACA AAIA™ Study Guide, a " Data Governance Framework " is the foundational control. It provides the policies, roles (stewards, owners), and standards necessary to manage the entire data lifecycle. Without a framework, activities like inventorying (Option C) or labeling (Option D) are ad-hoc and lack accountability. A formal framework ensures that data management is consistent, compliant with privacy laws, and aligned with the organization's risk tolerance. It is the " enabling " control that makes all other data quality and security metrics meaningful and enforceable.


NEW QUESTION # 176
A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?

Answer: B


NEW QUESTION # 177
An organization developed an AI model trained on its monthly data. Which of the following would be the BEST validation method to avoid data drift?

Answer: A

Explanation:
When dealing with monthly or temporal data, standard random splits (Option D) or cross-validation (Option A) can cause " temporal leakage, " where the model inadvertently learns from future data to predict the past.
According to ISACA AAIA™ principles, " Time Series " validation is the most appropriate method for sequential data. It involves training the model on a specific period (e.g., months 1-10) and testing it on the subsequent period (e.g., month 11). This approach accurately reflects how the model will perform in production and is essential for detecting data drift, as it identifies when seasonal trends or long-term shifts in customer behavior cause the model ' s accuracy to degrade over time.


NEW QUESTION # 178
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