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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit (AAIA) Exam
Exam Number:	AAIA
Related Certifications:	CIA CPA CISA
Exam Duration:	120 minutes
Exam Price:	USD 459 (member), USD 599 (non-member)
Exam Format:	Multiple-choice, Remote proctoring or test center, Closed-book, Computer-based
Passing Score:	65%
Available Languages:	English
Real Exam Qty:	55
Certificate Validity Period:	Not publicly specified (requires ongoing CPE maintenance after certification)
Recommended Training:	ISACA AI Audit Training Resources Official AAIA Training Course Providers
Exam Registration:	ISACA AAIA Certification Page
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Computer-based exam delivered via PSI test centers or remote proctoring
Pre Condition:	Must hold an active CISA, CIA, CPA, or equivalent ISACA-approved advanced auditing certification
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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.

ISACA Advanced in AI Audit Sample Questions (Q202-Q207):

NEW QUESTION # 202
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?

A. Using adversarial testing to simulate scenarios that stress test the model's predictions
B. Applying overrides to allow healthcare professionals to correct the AI model's recommendations
C. Conducting periodic model retraining to ensure alignment with updated patient data
D. Comparing incoming patient data distributions with the training data set

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 # 203
An IS auditor learns that an organization uses AI facial recognition technology for security purposes. Which of the following is the GREATEST ethical concern with this practice?

A. Potential bias against certain demographic groups
B. Absence of global standards for the use of AI in biometric security
C. Difficulty of enforcing access controls to facial recognition data
D. Individuals beginning to interact more cautiously

Answer: A

Explanation:
According to the ISACA AAIATM Study Guide, facial recognition systems are prone to significant ethical risks related to fairness and bias. These systems often exhibit higher error rates for specific demographic groups--particularly based on race, gender, and age--due to non- representative training datasets. For an IS auditor, this represents a major compliance and reputational risk, as biased outcomes can lead to discriminatory access or false accusations.

NEW QUESTION # 204
An organization has introduced an AI chat system where customers can enter their preferences and the system returns the best product selections. Which of the following is the BEST way to mitigate the risk of the system providing suggestions that may upset customers?

A. Conduct threat analysis to identify unknown exposures.
B. Perform testing of diverse scenarios to confirm outputs are within the acceptable range.
C. Increase the volume of training data to ensure the data set is fair and impartial.
D. Implement continuous monitoring of AI servers to detect anomalies in technical performance.

Answer: B

Explanation:
The risk described is that customer-facing suggestions may be inappropriate, insensitive, or offensive. The BEST mitigation is to perform testing of diverse scenarios (B)--including edge cases, demographic variations, and sensitive contexts--to confirm that outputs remain within acceptable business, ethical, and customer-experience thresholds. AAIA highlights scenario- based testing and fairness/impact assessments as key practices, especially where recommendations directly influence customer interactions.

NEW QUESTION # 205
During an audit of a bank ' s AI credit scoring system, an IS auditor discovers that applicants were not informed about automated decision-making. Which of the following should the auditor do FIRST?

A. Conduct an explainability assessment.
B. Evaluate appeal processes.
C. Prepare an audit report.
D. Evaluate transparency controls.

Answer: D

Explanation:
Transparency is a fundamental legal and ethical requirement for AI systems, particularly under regulations like GDPR, which mandate that data subjects be informed of automated decision-making. If an auditor finds that applicants were not informed, the immediate " First " step is to " Evaluate transparency controls " to determine why the notification process failed and to assess the scope of the non-compliance. This includes reviewing user agreements, privacy notices, and communication procedures. Once the failure is understood and the risk assessed, the auditor can move on to evaluating the appeal process (Option C) or preparing the final report (Option B).

NEW QUESTION # 206
An IS auditor is testing an AI model used for determining insurance premiums and eligibility. Which of the following is the MOST effective testing method to identify bias in algorithm outputs?

A. Cross-cluster analysis
B. Predictive analytics
C. Disparate impact analysis
D. Regression testing

Answer: C

Explanation:
Disparate impact analysisis a specific technique used to detect whether model decisions disproportionately disadvantage certain protected or sensitive groups (e.g., by gender, age, ethnicity, or other attributes). For an AI model determiningpremiums and eligibility, fairness and non-discrimination are critical regulatory and ethical requirements, and AAIA content highlights fairness and bias evaluation as core elements of AI governance and risk management.
Regression testing (A) checks that changes do not introduce defects in previously functioning components, not fairness. Cross-cluster analysis (B) may reveal patterns but is not inherently a fairness test. Predictive analytics (D) is a broad term for forward-looking analysis, not a method specifically designed to detect bias.
Therefore,disparate impact analysisis the most appropriate and targeted method to identify bias in the insurance AI model's outputs.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (bias, fairness, and transparency considerations).
ISACA AI ethics and fairness guidance used in AAIA training materials.

NEW QUESTION # 207
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