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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Risk | 33% | - Ethics, Regulations, and Standards for AI - AI Governance and Program Management - AI Risk Management - Privacy and Data Governance Programs - AI Models, Considerations, and Requirements |
| Topic 2: AI Operations | 46% | - Third-Party and Supply Chain Risk - Performance Monitoring and Evaluation - Incident Management and Resilience - AI System Lifecycle and Deployment - Operational Controls and Readiness |
| Topic 3: AI Auditing Tools and Techniques | 21% | - AI Audit Outputs and Reporting - Audit Testing and Sampling Methodologies - Audit Planning and Design - Audit Evidence Collection Techniques - Data Quality and Analytics for AI Audit |
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NEW QUESTION # 74
An organization deploys an AI-based image recognition system that is vulnerable to evasion attacks. Which of the following approaches BEST helps to ensure the system mitigates these evasion attempts?
Answer: B
Explanation:
Evasion attacks occur when an attacker modifies input data (such as adding subtle noise to an image) to trick a model into misclassification. The AAIATM manual identifies "Adversarial Training" as a primary defense, where the model is intentionally exposed to adversarial examples during the training phase to improve its robustness and resilience. This allows the model to learn the patterns associated with malicious inputs.
NEW QUESTION # 75
Which of the following is the BEST way to support the development and design of high-risk AI systems?
Answer: A
Explanation:
The AAIA™ Study Guide emphasizes that the foundation of any high-performing and ethical AI system lies in the quality and integrity of its data. For high-risk AI systems, such as those used in healthcare, finance, or criminal justice, it is essential to base models on trustworthy data. This ensures reliable predictions, reduces bias, and mitigates risk.
"Trustworthy datasets are characterized by accuracy, completeness, consistency, and ethical sourcing. In high- risk AI applications, ensuring data quality at every stage is crucial to system reliability and compliance." While backups, user training, and MFA are important for security and operational resilience, they do not address the core challenge of ensuring model accuracy and fairness at the development and design phase.
Therefore, option C is the most effective practice.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Governance and Management"
NEW QUESTION # 76
A car rental company is developing an AI system to dynamically adjust rental pricing based on demand, location, and customer profiles. Which of the following is the MOST important reason to conduct specific testing during development?
Answer: D
Explanation:
Dynamic pricing algorithms can unintentionally discriminate against protected groups if trained on biased data or poorly designed features. The AAIA highlights fairness testing as a mandatory requirement in any AI solution that impacts customers financially or socially.
Specific ethical tests are needed to ensure:
* Pricing does not vary unfairly based on demographics
* Sensitive attributes ( ethnicity, age, gender ) are not inferred or misused
* Customer segmentation does not disproportionately disadvantage protected groups
* Historical biases do not propagate into automated pricing
Options A, B, and C are important development tasks but do not address the highest-risk area: preventing discriminatory pricing. Fairness evaluation is a critical AAIA requirement.
References:
AAIA Domain 5: Ethical AI, Fairness, Discrimination Testing
AAIA Domain 1: AI Governance and Impact Assessments
NEW QUESTION # 77
When reviewing contracts or other lengthy documentation in the planning phase, which of the following tools would BEST extract relevant information?
Answer: D
Explanation:
Natural Language Processing (NLP) enables systems to understand, summarize, and extract relevant insights from unstructured text data such as contracts, emails, or reports. The AAIATM Study Guide recognizes NLP as the optimal tool for document review during audit planning or risk assessment phases.
"NLP techniques can efficiently process large volumes of documentation, highlight key clauses, and identify inconsistencies or risk indicators. This enhances audit productivity and thoroughness."
NEW QUESTION # 78
To confirm the fairness of AI model decisions, the BEST way to collect reliable evidence during an AI audit is by:
Answer: A
Explanation:
Testing the AI model with a curated and representative sample data set allows auditors to directly evaluate the fairness and bias of model decisions. This approach is aligned with best practices outlined in the AAIA™ Study Guide, as it enables quantifiable analysis of model behavior across different demographics or input scenarios.
"To assess fairness, auditors should use controlled data sets to evaluate whether model outputs disproportionately impact specific groups. This empirical testing provides stronger evidence than qualitative methods." While metadata (A) and developer interviews (C) can supplement findings, only B provides objective, reproducible evidence. Option D may reflect real-world interactions but lacks the control and consistency required in an audit.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Fairness and Bias Testing in AI Systems"
NEW QUESTION # 79
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