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| Section | Objectives |
|---|---|
| Topic 1: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Topic 2: AI Risk Management | - Risk identification and assessment for AI systems
|
| Topic 3: AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Topic 4: AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Topic 5: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
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NEW QUESTION # 144
Which of the following is the BEST justification for selecting a risk avoidance strategy when considering whether to deploy a high-impact AI system?
Answer: D
Explanation:
Risk avoidance is the risk treatment strategy of not engaging in an activity because the risks it presents cannot be adequately mitigated to within acceptable tolerance. For high-impact AI systems, the justification for avoidance must be proportionate to the gravity of the decision to forgo deployment entirely.
Why A is Correct: The ISACA AAIR risk treatment framework identifies potential harm to stakeholders as the most compelling justification for risk avoidance in AI deployment decisions. When a high-impact AI system poses risks of significant harm to individuals, communities, or society that cannot be adequately controlled, avoiding deployment is the ethically and legally appropriate choice. Stakeholder harm-especially irreversible or widespread harm-represents the highest severity risk outcome and justifies the most conservative risk treatment.
Why B is Wrong: Cost reduction objectives are business case considerations, not risk management justifications. Avoiding deployment to reduce costs is a financial decision, not a risk avoidance strategy. Risk avoidance decisions are driven by harm potential, not cost efficiency.
Why C is Wrong: Staff expertise shortages represent an organizational capability constraint that can be addressed through hiring, training, or managed services. A capability gap is a surmountable operational challenge, not a justification for permanently avoiding a valuable deployment.
Why D is Wrong: Data poisoning attack likelihood is a security risk that can be mitigated through appropriate controls-data integrity verification, provenance tracking, anomaly detection. A manageable risk with available mitigations does not justify full risk avoidance when stakeholder harm is not at stake.
NEW QUESTION # 145
Which of the following BEST helps to ensure a deep learning model with a large volume of relevant data meets an organization's needs?
Answer: A
Explanation:
Deep learning models have numerous hyperparameters-learning rate, batch size, regularization parameters, network architecture choices-that control how the model learns from data. Fine-tuning these parameters optimizes model performance for the specific dataset and task requirements.
Why D is Correct: According to ISACA AAIR model development guidance, when a large volume of relevant data is already available, hyperparameter fine-tuning is the most effective technique for ensuring the model meets organizational needs. It systematically optimizes the learning process to maximize performance on the specific problem, calibrating accuracy, generalization, and efficiency to the organization's requirements.
Why A is Wrong: A federated accountability model is a governance structure, not a technical method for optimizing AI performance. It addresses how responsibility is distributed, not how the model learns.
Why B is Wrong: Unsupervised learning is a class of ML approaches used when labeled data is unavailable. It does not address optimization of a deep learning model where relevant data is already present.
Why C is Wrong: Data augmentation artificially expands training datasets through transformations-useful when data is scarce. With a large volume of relevant data already available, augmentation provides minimal additional benefit and hyperparameter optimization becomes the more impactful intervention.
NEW QUESTION # 146
Which of the following is the BEST way for a risk practitioner to communicate rapidly evolving changes to the organization ' s risk profile resulting from the use of generative AI?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Rapidly changing generative-AI risk is best communicated through concrete real-world incidents and business impacts that stakeholders can understand. Technical root causes or exhaustive lists can obscure the practical consequences that decision-makers need to evaluate. This makes option B, Provide real-world examples of risk impacts from generative AI incidents and breaches, 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 # 147
A risk practitioner discovers that an AI chatbot stores user interactions indefinitely. Which of the following is the MOST appropriate mitigation?
Answer: C
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. Indefinite chatbot retention creates unnecessary privacy and breach exposure. Automated retention limits linked to documented consent or purpose ensure records are kept only as long as justified while still accommodating legitimate legal, operational, or audit requirements. This makes option B, Automate retention limits linked to documented consent, 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 # 148
Which of the following is the MOST important consideration when selecting an AI governance framework for a civil aviation system?
Answer: C
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. Civil aviation is safety-critical, so governance must preserve qualified human oversight, intervention, escalation, and override capability. Technical simplification, privacy, and centralization are relevant but do not replace human accountability for consequential decisions. This makes option A, Provisions for human oversight, 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 # 149
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