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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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	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 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.

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ISACA Advanced in AI Audit Sample Questions (Q143-Q148):

NEW QUESTION # 143
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?

A. Performing a business impact analysis (BIA) to assess the consequences of AI model failure
B. Reviewing the organization's AI software development life cycle documentation
C. Conducting a penetration test to identify vulnerabilities in the model
D. Assessing data lineage to verify the traceability of data sources

Answer: D

Explanation:
Assessing data lineage provides insight into the origin, flow, and transformation of data across its lifecycle, which is crucial for validating data governance. The AAIA™ Study Guide states that data lineage is essential to ensure the accuracy, consistency, and trustworthiness of data used in training AI models.
"Traceability of data sources is a core tenet of effective data governance. Data lineage validation ensures data quality, prevents unauthorized modifications, and maintains auditability." BIA (A) focuses on impact, not data quality. Reviewing SDLC (B) is broad and may not highlight data- specific risks. Penetration testing (C) addresses security, not governance. Therefore, D is the best method.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Quality, Integrity, and Governance Practices"

NEW QUESTION # 144
Which of the following is the PRIMARY objective of AI governance?

A. Promoting a positive return on investment (ROI) from AI projects
B. Implementing compliance and ethics controls for AI initiatives
C. Ensuring controls over AI are designed well and operate effectively
D. Defining clear roles and responsibilities for AI development, use, and oversight

Answer: D

NEW QUESTION # 145
An IS auditor identifies that an AI model occasionally invents nonexistent medical test results. Which of the following recommendations would BEST mitigate this risk?

A. Increasing the model context
B. Enabling frequency penalties on rare words
C. Increasing the temperature
D. Decreasing the top-p sampling

Answer: D

Explanation:
Inventing nonexistent medical test results is a form ofhallucinationcommonly associated with generative AI models.Reducing top-p sampling(A) restricts the model to choosing from the most probable next tokens, reducing randomness and preventing the generation of fabricated or implausible content. AAIA emphasizes configuration tuning (temperature, top-k, top-p) as critical controls for mitigating hallucinations.
Increasing context (B) helps provide more information but does not directly reduce hallucinations. Increasing temperature (C) would make hallucinationsworseby adding randomness. Frequency penalties (D) discourage repetition, not hallucinations. The strongest mitigation isreducing sampling randomnessvia reduced top-p.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (generative AI safeguards, hallucination mitigation).

NEW QUESTION # 146
An organization uses an AI-powered tool to detect and respond to cybersecurity threats in real time. An IS auditor finds that the tool produces excessive false positives, increasing the workload of the security team. Which of the following techniques should the auditor recommend to BEST evaluate the tool's effectiveness in managing this issue?

A. Conduct penetration testing to assess the system's ability to detect genuine threats.
B. Implement a benchmarking tool to compare the system's alerting capability with industry standards.
C. Use a log analysis tool to examine the types and frequency of alerts generated.
D. Deploy a machine learning (ML) validation tool to increase the model's accuracy and performance.

Answer: D

NEW QUESTION # 147
Which of the following techniques BEST supports machine learning (ML) training in sentiment analysis?

A. Log-rank test
B. Logistic regression
C. Image recognition
D. Analysis of variance

Answer: B

Explanation:
Sentiment analysis is a classification task where text is categorized (e.g., positive, negative, or neutral).
Logistic regression is a fundamental and highly effective statistical method for binary or multi-class classification, making it a standard choice for sentiment models. It estimates the probability of an input belonging to a specific class. While more complex architectures like Transformers (LLMs) are now common, logistic regression remains a benchmark for its simplicity and transparency in audit contexts. Image recognition and ANOVA are not relevant to the linguistic processing required for sentiment analysis, and the log-rank test is typically used in survival analysis rather than NLP classification.

NEW QUESTION # 148
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