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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 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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q106-Q111):

NEW QUESTION # 106
Which of the following provides the BEST evidence that an organization's AI integration is aligned with its overall technology strategy?

Answer: B

Explanation:
The BEST evidence of alignment between AI integration and technology strategy is thatclear KPIs(A) have been defined for AI systems and tied to strategic objectives, measurable outcomes, and governance oversight.
KPIs demonstrate that AI initiatives are monitored, performance is evaluated, and outcomes align with enterprise value.
Policies (B) are foundational but do not demonstrate strategic alignment. Innovation culture (C) is broad and subjective. AI expertise (D) indicates capability, not alignment. AAIA stresses linking AI outcomes to business goals throughmetrics, monitoring, and governance dashboards.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Strategy Alignment.


NEW QUESTION # 107
During an audit of an investment organization's AI-powered software, an IS auditor identifies a potential security risk. What is the GREATEST risk associated with staff exfiltrating organizational data to a generative AI tool?

Answer: D


NEW QUESTION # 108
An IS auditor notes the combined number of records utilized within the training, validation, and testing data sets exceeds the total number of records in the original data set. Which of the following is MOST important for the auditor to determine?

Answer: B

Explanation:
If the combined size of the training, validation, and testing sets exceeds the original data size, it suggests that records may have been reused across sets. This can lead to data leakage, where the model has access to test or validation information during training, resulting in overly optimistic performance metrics.
"Data leakage invalidates model evaluation because it introduces unintended data overlap. Auditors must ensure that the training, validation, and test sets are strictly partitioned." Options A, C, and D refer to process order or quantity, but only B addresses the root issue of compromised model integrity due to overlapping data.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Partitioning and Leakage Risks"


NEW QUESTION # 109
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 Audit™ (AAIA™) 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.
Themost effective approach to detect data driftis tocontinuously compare the statistical distributions of incoming (production) data with those of the training dataset. 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 AAIA™ 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." While options such as retraining the model (option C) or adversarial testing (option D) are valuable for ongoing performance and robustness,they do not inherently detect data drift-they respond to or stress-test existing issues. Applying overrides (option B) is a human-in-the-loop safeguard, not a method for drift detection.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Monitoring and Maintenance," Subsection: "Detection and Management of Data Drift"


NEW QUESTION # 110
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 apredictive AI tool analyzing abnormalities, the GREATEST concern is therate 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.
Integration ease (B), speed (C), and cost (D) are important practical considerations but are secondary to whether the toolaccurately identifies or misses significant anomalies. Therefore, error behavior-false positives and false negatives-represents the primary risk to audit quality.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI in Audit Processes; Domain 2: AI Operations (model performance metrics and risk).
ISACA analytics guidance on evaluating AI tools using precision, recall, and error analysis in audit contexts.


NEW QUESTION # 111
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