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| Section | Objectives |
|---|---|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
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| AI Risk Management | - Risk identification and assessment for AI systems
|
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NEW QUESTION # 26
Which of the following testing approaches BEST helps to identify risk in an AI-powered customer support solution?
Answer: A
Explanation:
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. Adversarial testing deliberately supplies malformed, misleading, or hostile inputs to reveal failure modes that ordinary accuracy or load testing may miss. This is especially important for customer-facing AI where unexpected inputs can manipulate or degrade outputs. This makes option A, Using adversarial inputs to determine failure modes, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 27
Which of the following is the PRIMARY purpose of maintaining comprehensive model cards and documentation?
Answer: B
Explanation:
Model cards are standardized documents that communicate key information about AI models, including their intended use, training data, performance characteristics, limitations, and ethical considerations. They serve as a primary transparency instrument in AI governance.
Why D is Correct: According to the ISACA AAIR curriculum, the primary purpose of model cards is to provide transparency to stakeholders-including developers, users, auditors, and regulators. Transparency enables informed decision-making about model deployment, helps identify potential misuse, and supports responsible AI governance across the life cycle.
Why A is Wrong: Justifying use cases is a secondary benefit. Model cards are not primarily advocacy documents; their core function is objective disclosure of model characteristics and limitations.
Why B is Wrong: Preserving audit trails is a governance function served by version control and change management systems. While model cards contribute to audit readiness, it is not their primary purpose.
Why C is Wrong: Technical specifications represent only a subset of model card content. Model cards go beyond technical detail to address fairness, bias, intended use boundaries, and societal impact considerations.
NEW QUESTION # 28
A rideshare organization is considering the use of AI for dynamic surge pricing. Which of the following is the MOST important risk to evaluate?
Answer: D
Explanation:
Within the ISACA Advanced in AI Risk framework, governance decisions should align AI use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal or ethical obligations. Dynamic surge pricing directly affects customers and market behavior, creating legal and competition-law exposure that can be more consequential than integration, compute, or ordinary model-staleness issues. Regulatory scrutiny can lead to penalties and restrictions. This makes option B, Potential regulatory inquiries for anti- competitive AI pricing practices, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 29
A risk practitioner is reviewing the training data collection process for a new AI model. Which practice best reduces the risk of model bias?
Answer: B
Explanation:
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. Training- data collection should prioritize representative and relevant samples that reflect the intended population and use case. More data is not automatically better, normalization occurs later, and public datasets can be used if their quality, provenance, and suitability are properly assessed. This makes option A, Ensure input samples are representative and relevant, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 30
Which of the following is the GREATEST risk when an organization lacks clearly defined accountability mechanisms for AI outputs and decisions?
Answer: C
Explanation:
AI systems make decisions that can affect individuals, organizations, and society. When no individual or function is clearly accountable for those decisions, the organization cannot demonstrate due diligence, remedy harms, or mount a coherent legal defense when challenged.
Why D is Correct: The ISACA AAIR framework identifies legal liability as the greatest organizational risk from absent accountability mechanisms. When AI outputs cause harm-discriminatory lending decisions, unsafe autonomous vehicle actions, inaccurate medical diagnoses-the absence of documented accountability makes it impossible to demonstrate responsible governance to courts, regulators, and affected parties. This creates maximum legal exposure across contract, tort, and regulatory law.
Why A is Wrong: Intellectual property exposure is a significant risk in AI contexts (particularly around training data and model weights) but is not primarily caused by absent accountability mechanisms. IP risk arises from access controls and contractual protections.
Why B is Wrong: Ineffective model training is a technical quality issue. While accountability for model development may influence training quality, ineffective training is not the primary risk from absent accountability for outputs and decisions.
Why C is Wrong: Reduced availability is an operational resilience concern. Accountability gaps do not directly cause availability failures, which are driven by architectural and operational factors.
NEW QUESTION # 31
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