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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Available Languages:	English
Passing Score:	65%
Related Certifications:	CISA CPA ACCA CIA FCCA
Exam Format:	Multiple Choice, Computer-Based, Remotely Proctored
Exam Duration:	120 minutes
Real Exam Qty:	90
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Online remotely proctored computer-based exam
Pre Condition:	Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA.
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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.

ISACA Advanced in AI Audit Sample Questions (Q238-Q243):

NEW QUESTION # 238
Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights?

A. Model runtime efficiency logs
B. Data usage agreements
C. Use of open-source intellectual property
D. Data performance metrics

Answer: B

Explanation:
To assess compliance with intellectual property (IP) and data rights, the IS auditor must review documented data usage agreements that specify ownership, licensing, consent, and limitations of use. The AAIA™ Study Guide underscores the importance of verifying that the data used to train or feed AI models is obtained and utilized within legal and contractual boundaries.
"Auditors must review data usage agreements to validate whether the organization has appropriate rights to use, distribute, or transform data inputs, especially where third-party or sensitive data is involved." While open-source usage (C) is a concern, only B provides legal clarity. Metrics (A) and logs (D) reflect performance-not legal compliance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Rights, Licensing, and Intellectual Property"

NEW QUESTION # 239
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?

A. Suspend the algorithm until concerns are addressed.
B. Obtain users' consent for the content they wish to view.
C. Introduce controls allowing individuals to customize content preferences.
D. Regularly audit and adjust algorithms to reduce biases.

Answer: D

Explanation:
The AAIATM Study Guide emphasizes that regular algorithmic audits are critical for identifying unintended consequences such as the promotion of harmful or biased content. This proactive approach helps maintain trust and ensure that algorithmic decisions align with organizational values and ethical standards.
"Auditing and monitoring AI models regularly helps detect and correct bias, drift, or other unintended behavior. It is essential for high-impact AI systems like content recommendation engines."

NEW QUESTION # 240
An organization shares an AI model with external partners. One partner reports that sensitive data has been inadvertently exposed through the model's outputs. Which of the following is the IS auditor's BEST recommendation?

A. Retrain the model immediately and implement privacy-preserving techniques.
B. Limit the model's outputs to anonymized results while investigating further.
C. Disable the shared model and notify partners of the potential breach.
D. Audit the data pipelines of all partners to identify the source of the leak.

Answer: C

NEW QUESTION # 241
Which of the following is the GREATEST data quality risk when using an AI tool to assist with audit procedures?

A. Embedding AI audit tools in transactional systems without user training
B. Training models on historical audit results generated prior to AI adoption
C. Utilizing unstructured data sources without standardized preprocessing
D. Applying automated anomaly detection without human oversight

Answer: C

Explanation:
Unstructured data without standardized preprocessing (option A) creates the highest data quality risk because AI models depend heavily on the cleanliness, consistency, and structure of input data.
AAIA warns that improperly processed unstructured data leads to:
Incorrect text extraction
Lost contextual meaning
Feature extraction errors
Misclassification
Inaccurate audit evidence

NEW QUESTION # 242
An AI tool is being implemented for a regional healthcare organization. Which of the following training methods BEST ensures the AI output does not reveal whether someone's personal data was used?

A. Data augmentation during training to improve privacy
B. Supervised learning with labeled patient records
C. Transfer learning using public health data sets
D. Differential privacy applied during model training

Answer: D

Explanation:
Differential privacy introduces carefully calibrated noise during training or query responses so that it becomes mathematically difficult to infer whether any specific individual's record is included in the training set. For healthcare data--highly sensitive and subject to strict privacy laws--this technique directly supports privacy-by-design, reducing the risk that model outputs leak membership information or reconstruct personal records.

NEW QUESTION # 243
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