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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 (Q271-Q276):

NEW QUESTION # 271
An organization deploys an AI recruitment platform to screen job applicants. The IS auditor identifies that the platform's decisions may be influenced by model bias. Which of the following risk mitigation strategies is BEST for the auditor to recommend?

Answer: C


NEW QUESTION # 272
Which of the following is the MOST important AI data governance method to protect privacy and data security?

Answer: B

Explanation:
Periodic monitoring is the MOST critical governance mechanism for protecting privacy and data security in AI systems, especially those handling sensitive or personal data. ISACA's AAIA guidance emphasizes that data governance must be continuous because AI systems evolve, data drifts, risk exposures shift, and privacy threats increase over time.
Periodic monitoring ensures:
* Detection of unauthorized access
* Identification of anomalous data use
* Confirmation that privacy controls remain effective
* Early detection of data misclassification or leakage
* Verification of compliance with retention, deletion, and minimization requirements Options A and C do not protect privacy. Acceptable use policies (B) provide guidelines but do not enforce ongoing protection. Continuous monitoring is the operational safeguard that enforces privacy and security controls at all times.
References:
AAIA Domain 1: Data Governance Controls
AAIA Domain 5: Privacy and Data Protection Monitoring


NEW QUESTION # 273
A retail organization uses an AI model to forecast inventory based on customer purchasing trends and updates the model quarterly. The model recently failed to recognize a surge in demand during a popular shopping season. Which of the following issues does this situation BEST demonstrate?

Answer: D

Explanation:
Data drift occurs when the statistical properties of input data change over time, impacting the accuracy of an AI model. In this case, the model failed to adapt to recent demand trends, indicating that the data on which it was trained no longer reflects current behavior.
"Data drift leads to performance degradation in AI systems when real-world input data shifts away from training data patterns. Monitoring for drift is essential, particularly in dynamic environments such as retail." Although training diversity (A) and outlier detection (D) are relevant to accuracy, only B addresses the temporal misalignment between training data and real-time data. Overfitting (C) would more likely cause poor generalization in other contexts.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Monitoring and Data Drift Detection"


NEW QUESTION # 274
An IS auditor is looking to expedite reporting for an audit with complex issues. Which of the following would be the MOST effective way for the auditor to use generative AI?

Answer: C

Explanation:
The AAIA™ Study Guide highlights that generative AI tools can significantly enhance audit productivity, especially in content generation tasks. Drafting executive summaries-condensing complex findings into clear, concise formats-is a high-impact use case that benefits from generative AI's natural language generation capabilities.
"Generative AI can be leveraged by auditors to automate reporting sections, such as executive summaries, ensuring consistency and saving time without compromising content clarity." While A, C, and D are valid uses, B delivers the most value by expediting a time-intensive and high-level reporting task in complex audits.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Generative AI Applications in Audit Reporting"


NEW QUESTION # 275
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: C

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. Once the failure is understood and the risk assessed, the auditor can move on to evaluating the appeal process (Option C) or preparing the final report (Option B).


NEW QUESTION # 276
......

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