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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
  • 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.
Topic 3
  • 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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ISACA Advanced in AI Audit Sample Questions (Q207-Q212):

NEW QUESTION # 207
An AI social media platform uses an algorithm to increase user engagement that could unintentionally promote divisive content. Which of the following is the BEST course of action to mitigate this risk?

Answer: C


NEW QUESTION # 208
An IS auditor examining change management procedures for an AI system observes inconsistent training data validation and verification protocols prior to model retraining. Which of the following is the MOST significant risk in this context?

Answer: A

Explanation:
When training data validation is inconsistent, the most severe risk is that the AI model may learn from incorrect, incomplete, biased, or corrupted data. This directly leads to a degradation of system reliability (option C), which manifests as inaccurate predictions, higher error rates, bias, or unstable behavior.
AAIA emphasizes that data validation prior to retraining is one of the most important controls because model behavior is fully dependent on training data integrity. If the quality and correctness of the data cannot be guaranteed, the resulting model outputs become unreliable, which can undermine compliance, operational decisions, and user trust.
Option A is less critical because increased complexity is not the core risk. Option B is important but secondary; documentation issues do not inherently degrade model reliability. Option D is an efficiency issue, not a risk to output integrity.
Therefore, compromised reliability due to poor-quality training data is the most significant risk.
References:
AAIA Domain 2: Data Management Specific to AI (data validation, verification, data quality).
AAIA Domain 1: Governance and Risk Controls for AI.


NEW QUESTION # 209
Which of the following should be an IS auditor's GREATEST concern when using a predictive AI tool to analyze data abnormalities?

Answer: B

Explanation:
For a predictive AI tool analyzing abnormalities, the GREATEST concern is the rate and impact of false positives and false negatives (A). False positives can lead to unnecessary investigation, while false negatives mean true issues (e.g., fraud, control failures) remain undetected. From an assurance perspective, false negatives are especially critical because they directly undermine audit objectives. AAIA underscores that key performance metrics (e.g., precision, recall) and error trade-offs are essential in evaluating AI tools used in audit.


NEW QUESTION # 210
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?

Answer: C

Explanation:
Ensuring that input data is appropriate and relevant (option D) is the most critical factor in preventing hallucinations--where generative models produce fabricated or misleading outputs.
The AAIATM Study Guide notes, "Generative models are highly sensitive to input data; inaccurate, irrelevant, or inappropriate inputs increase the likelihood of nonsensical or incorrect outputs." While bias detection, data quality audits, and anonymization are important, ensuring the relevance and suitability of input data is foundational for reliable generative AI performance.


NEW QUESTION # 211
An IS auditor is auditing an AI system that predicts inventory needs. The system recently failed to predict a stock outage for a key product. Which of the following audit tests would BEST validate the system's accuracy?

Answer: A

Explanation:
The best way to validate the accuracy of a predictive AI system is to use historical testing with past sales data (option D). According to the AAIA™ Study Guide, "historical (or back-testing) is essential for evaluating how well a model would have performed using actual data from previous periods, directly reflecting its predictive validity." This method reveals any gaps or biases in the model by comparing predictions to known outcomes.
Unit testing, load testing, and sensitivity analysis are useful for technical verification and robustness but do not provide direct evidence of prediction accuracy in real-world scenarios.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Validation Techniques"


NEW QUESTION # 212
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