ISACA AAIA Exam Questions [2026]-Achieve Highest Scores

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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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.

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ISACA Advanced in AI Audit Sample Questions (Q192-Q197):

NEW QUESTION # 192
Which of the following techniques would be MOST effective as part of incident management procedures for a prompt injection attack?

Answer: C

Explanation:
Prompt injection attacks involve maliciously crafted inputs intended to override system instructions, exfiltrate data, or cause harmful behavior. The most effective control aligned with incident management is to deploy robust input validation and sanitization (C), which includes rules and filters designed to detect and neutralize potentially malicious content before it reaches the model. AAIA's coverage of AI threats and vulnerabilities highlights the importance of input validation and secure prompt handling for generative AI systems.


NEW QUESTION # 193
An organization is developing an AI system that integrates data from multiple external sources without clearly defined data ownership policies. Which of the following is the GREATEST concern in this situation?

Answer: C

Explanation:
When integrating data from multiple external sources, uncleardata ownershipdirectly affects accountability for privacy, consent, retention, and lawful processing. This createsgaps in AI privacy compliance and accountability(option D), which is the greatest concern because violations can lead to regulatory sanctions, litigation, and serious reputational damage. AAIA's governance and risk domain emphasizesprivacy and data governance programs, including clear roles, responsibilities, and ownership.
Option A is important but relates more to accuracy and performance validation, not the foundational legal and accountability issues. Option B (access permissions) is a control issue but usually easier to remediate once ownership and responsibilities are clarified. Option C (dependence on automated data collection/cleansing) can introduce quality risks but does not directly resolve or define who is accountable. Thus, the primary risk is the lack of clear privacy and accountability structures across external data sources.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Privacy and Data Governance Programs (data governance, privacy considerations).
ISACA guidance on AI governance, roles, responsibilities, and accountability for data use.


NEW QUESTION # 194
Which technique is MOST useful for identifying whether a model's outputs disproportionately disadvantage a protected group?

Answer: A

Explanation:
Fairness metrics (e.g., demographic parity, equalized odds) are specifically designed to compare outcome rates across subgroups, revealing disparate impact that a generic accuracy metric would mask.


NEW QUESTION # 195
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?

Answer: A


NEW QUESTION # 196
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?

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.


NEW QUESTION # 197
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