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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 (Q170-Q175):

NEW QUESTION # 170
Which of the following is the GREATEST risk when training data is not separated into distinct training and testing sets?

Answer: C

Explanation:
If training and testing sets are not separated, the model evaluates itself on the same data it was trained on, creating a false sense of accuracy. The result isoverfitting(option A): the model learns the training data too well and fails to generalize to new data.
AAIA emphasizes that proper data splitting is foundational to machine learning evaluation. Overfitting undermines real-world performance, creates untrustworthy predictions, and hides bias or errors.
Model drift (B) occurs after deployment. Hallucinations (C) relate more to generative models. Underfitting (D) occurs when the model is too simple, not from lack of dataset separation.
Thus, overfitting is the direct and greatest risk when training and testing sets are not segregated.
References:
AAIA Domain 2: Testing Techniques and Model Evaluation Standards.
AAIA Domain 1: Fundamentals of ML model training.


NEW QUESTION # 171
Which of the following controls would MOST effectively mitigate worst-case service disruption scenarios affecting an AI-based application system?

Answer: B

Explanation:
Including AI-specific disruption scenarios in the organization's Disaster Recovery Plan (DRP) ensures preparedness for worst-case events affecting AI systems. The AAIATM Study Guide recommends tailoring business continuity and DR strategies to cover model unavailability, data corruption, and AI-specific dependencies.
"AI disruptions can arise from unique causes such as model corruption, adversarial attacks, or drift. Integrating these into the DRP allows for effective, scenario-specific responses and minimizes downtime."


NEW QUESTION # 172
A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations. Which of the following MOST effectively detects data drift related to the model's predictions?

Answer: D

Explanation:
Detecting data drift is critical in maintaining the reliability and accuracy of AI models, especially in dynamic environments like healthcare where patient populations and data characteristics can change over time. According to the ISACA Advanced in AI AuditTM (AAIATM) Study Guide, data drift refers to changes in the input data's statistical properties compared to the data on which the model was originally trained. If not detected, data drift can degrade model performance and lead to erroneous predictions.
The most effective approach to detect data drift is to continuously compare the statistical distributions of incoming (production) data with those of the training data set. This allows organizations to identify deviations in data patterns, which can be early indicators that the AI model's predictions may no longer be valid or optimal.
As stated in the AAIATM Study Guide under "AI Model Monitoring and Maintenance":
"Monitoring input data for distributional changes compared to the model's training data is an essential step in identifying data drift. Statistical tests and visualizations can help auditors and AI operators detect when the underlying data characteristics have shifted, prompting further investigation or retraining needs."


NEW QUESTION # 173
What is the PRIMARY purpose of a "red team" exercise for an AI system?

Answer: C

Explanation:
AI red teaming simulates adversarial or malicious use to uncover weaknesses (jailbreaks, biased outputs, harmful content generation) before deployment, complementing traditional security testing.


NEW QUESTION # 174
After AI training data has been tested for biases, which of the following is MOST important to check to validate the effectiveness of the testing?

Answer: A

Explanation:
Even after identifying and mitigating bias, organizations must ensure that AI outputs do not create unacceptable risks.
AAIA emphasizes that bias mitigation must result in:
Fair outcomes
Justifiable predictions
No disproportionate harm to any demographic group
Alignment with organizational risk tolerance
Option B reflects this requirement, ensuring that the model's real-world impact aligns with documented risk thresholds.


NEW QUESTION # 175
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