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Isaca AAIA Exam Questions
- 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.
Free Isaca AAIA Exam Actual Questions
Note: Premium Questions for AAIA were last updated On Jul. 12, 2026 (see below)
The internal audit department of a large global organization is evaluating the use of an AI-based voice-to-speech tool to document interviews during audits. The tool uploads all recordings to a cloud service provider for transcription. Which of the following is the GREATEST risk?
Audit interviews often contain highly sensitive, proprietary, or even non-public information. Uploading these recordings to a cloud provider introduces the 'Risk of unauthorized access' by the vendor's employees or through a security breach at the vendor's site. According to ISACA, the loss of 'Confidentiality' over audit workpapers is a critical failure of professional standards. While inaccurate transcriptions (Option D) are a nuisance, they can be corrected by the auditor; however, once sensitive data is compromised by a third party, the damage is irreversible. Auditors must ensure the vendor has rigorous 'encryption' and 'at-rest' security attestations.
Which control is MOST important to verify in order to ensure proper data management with AI systems?
According to the ISACA AAIA Study Guide, a 'Data Governance Framework' is the foundational control. It provides the policies, roles (stewards, owners), and standards necessary to manage the entire data lifecycle. Without a framework, activities like inventorying (Option C) or labeling (Option D) are ad-hoc and lack accountability. A formal framework ensures that data management is consistent, compliant with privacy laws, and aligned with the organization's risk tolerance. It is the 'enabling' control that makes all other data quality and security metrics meaningful and enforceable.
Which of the following could be used to BEST identify underlying patterns in control effectiveness within unlabeled data elements?
When data is 'unlabeled' (meaning the outcomes or 'answers' are not provided), supervised methods like Random Forest (Option D) or XGBoost (Option A) cannot be used. 'Unsupervised learning' is specifically designed to discover 'underlying patterns,' clusters, or latent structures in data without human guidance. For an auditor, unsupervised techniques (like clustering) are invaluable for exploratory analysis, such as grouping similar control failures or identifying unusual transactional behaviors that have not yet been categorized as fraudulent or legitimate.
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?
In high-stakes financial applications like KYC, the primary concern is the potential business and regulatory impact of an AI error---such as false customer rejection or failure to detect fraudulent accounts. The AAIA Study Guide emphasizes aligning AI risk assessments with business impact and regulatory exposure.
''In financial institutions, the most material risk of AI errors lies in operational disruption and regulatory fines. KYC models must be assessed for how errors can lead to compliance failures or reputational harm.''
Benchmarking (B) supports best practice alignment, and incident response (C) is part of mitigation, but D addresses the most critical consequence of AI risks in banking.
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?
In predictive maintenance use cases---such as detecting turbine failure---the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIA Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified.
''Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives.''
Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate.
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