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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit
Exam Number:AAIA
Exam Duration:150 minutes
Available Languages:Spanish, English, Chinese
Related Certifications:CISA
FCCA
CIA
ACCA
CPA
Real Exam Qty:90
Exam Format:Computer-based testing, Multiple-choice questions, Scenario-based questions
Passing Score:450 (scaled, range 200–800)
Exam Price:US$459 (member) / US$599 (non-member)
Certificate Validity Period:3 years
Recommended Training:AAIA Review Manual
AAIA Official Review Course
Exam Registration:ISACA Official Registration
Sample Questions:ISACA AAIA Sample Questions
Exam Way:Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong)
Pre Condition:Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus
Official Syllabus URL:https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline

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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.

ISACA Advanced in AI Audit Sample Questions (Q181-Q186):

NEW QUESTION # 181
A bank's fraud detection model achieves high accuracy on its initial dataset but performs poorly in production. The data science team needs to tune hyperparameters and select the best model architecture. Which dataset is BEST to use for this selection process?

Answer: C

Explanation:
In the standard machine learning workflow, the "Validation Dataset" is specifically used for "model selection" and "hyperparameter tuning." It serves as a bridge between training and the final test.
Using the training set (Option B) for tuning would lead to overfitting. The "Testing" or "Holdout" set (Options A and C) must remain completely "unseen" until the very end to provide an unbiased final estimate of how the model will perform in the real world. According to the ISACA AAIATM manual, maintaining this strict partitioning is critical for model integrity and preventing overly optimistic performance reports.


NEW QUESTION # 182
Which of the following considerations should be prioritized when using an AI tool to select a sample for conducting an audit of a financial institution's transaction processing system?

Answer: D

Explanation:
In an audit context, transparency of sampling is essential for demonstrating that the sample is fair, unbiased, and aligned with the audit objectives. When an AI tool selects samples for testing financial transactions, auditors must be able to explain and defend how the sample was generated--particularly to management, regulators, and external stakeholders. Option A directly supports AAIA's focus on audit planning, sampling methodologies, and AI audit evidence.


NEW QUESTION # 183
Which of the following should be of GREATEST concern to an IS auditor when reviewing ethical considerations for an AI solution?

Answer: B

Explanation:
The GREATEST concern is when the AI system's decision-making process is unexplainable (A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.


NEW QUESTION # 184
Which of the following is the MOST important reason for measuring the AI system ' s performance against predefined metrics in a staging environment?

Answer: C

Explanation:
The AAIA™ Study Guide emphasizes the importance of a rigorous " Staging " or " User Acceptance Testing
" (UAT) phase in the AI lifecycle. Measuring performance against predefined metrics (such as Precision, Recall, or F1-Score) in a non-production environment is critical to " Validate that the system meets business and technical requirements prior to release " . This step prevents the deployment of models that may exhibit bias, inaccuracy, or logic errors in the real world. While privacy compliance (Option A) and dataset freshness (Option B) are vital, they are components of the broader validation process that ensures the model is functional, safe, and fit for purpose before impacting live operations.


NEW QUESTION # 185
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 AAIATM 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.


NEW QUESTION # 186
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