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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 Nov. 03, 2026 (see below)
The accuracy of an AI model used for product recommendations, trained on data from a specific year, is declining two years later as customer buying patterns have shifted toward sustainable products. Which of the following is the MOST likely cause?
Data drift (also known as model drift or concept drift) refers to the phenomenon where the statistical properties of the target variable or the input data change over time, leading to a decline in model performance. In this scenario, the fundamental shift in customer preferences toward sustainability represents a change in the real-world environment that the model's original training data no longer reflects. As stated in the AAIA guidelines, models operating in dynamic environments like retail require continuous monitoring to detect when historical patterns are no longer predictive. Covariate shift (Option D) is a sub-type of drift specifically involving changes in the distribution of input features, but 'Data drift' is the broader, most appropriate term for the observed performance degradation.
Which of the following should be done FIRST when developing an incident management process for AI threats?
The AAIA framework states that incident response begins with roles and responsibilities. Without clearly assigned accountability, no classification, escalation, or detection procedures can be effectively implemented.
Defining roles ensures:
Ownership of monitoring
Chain of command for incident decisions
Clear responsibility for documentation
Communication pathways
Allocation of resources for containment
Classification (A), escalation (D), and SIEM configuration (C) follow AFTER roles are assigned. Therefore, defining roles and responsibilities is foundational.
AAIA Domain 2: AI Incident Management
AAIA Domain 1: Governance and Accountability Structures
An organization using AI to create digital content faces challenges in protecting its intellectual property. Which of the following is the BEST way to mitigate this risk?
For AI-generated content (images, text, or audio), 'watermarking' is a critical technical control to protect Intellectual Property (IP) and ensure provenance. Digital watermarks can be embedded into the metadata or the content itself (often invisibly) to prove the organization is the creator and to track unauthorized distribution. This supports legal claims and brand protection. Firewalls and data separation protect the system, but watermarking protects the output once it is shared online or with clients. As AI makes content creation easier, the ability to prove ownership becomes an essential ethical and legal safeguard.
Which of the following is the GREATEST concern when an audit team relies on generative AI to create audit reports?
The greatest concern is that the generative model may hallucinate, producing incorrect facts or conclusions (option B). In an audit context, hallucinations can create false statements about control effectiveness, misreport risks, or incorrectly summarize evidence.
AAIA stresses that auditors must maintain professional skepticism and validate AI-generated content. Misstatements are high-risk because they undermine audit credibility, regulatory compliance, and organizational decision-making.
Formatting inconsistency (C) and generic language (D) are cosmetic issues. Outdated information (A) is a concern but does not inherently create false conclusions.
Hallucinated misinformation is the most severe and dangerous issue in AI-generated audit reporting.
AAIA Domain 3: AI in Audit Processes (accuracy of AI outputs, hallucination risks).
AAIA Domain 5: Ethical Responsibilities in AI-Assisted Work.
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?
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.
Option A uses real patient records directly and does not, by itself, mitigate inference risk. Option B (data augmentation) may expand the dataset but does not guarantee resistance to membership inference attacks. Option D (transfer learning using public data) can help, but if any private data is used in fine-tuning, privacy risks remain. Differential privacy, as in option C, is the most appropriate control to ensure that outputs do not reveal whether particular personal data was used.
ISACA, AAIA Exam Content Outline -- Domain 1: Privacy and Data Governance Programs; Domain 2: Data Management Specific to AI (data confidentiality, data security).
ISACA guidance on privacy-by-design and AI risk management concepts reflected in AAIA.
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