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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk identification and assessment for AI systems
|
| Topic 2: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 3: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| Topic 4: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 5: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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NEW QUESTION # 106
An organization plans to deploy an AI system that ingests multiple sources with varying completeness and accuracy. Which of the following is the risk practitioner's BEST recommendation?
Answer: C
Explanation:
Data quality directly determines AI model accuracy and reliability. When input sources vary in completeness and accuracy, the AI system is exposed to continuous data quality risks that can produce unreliable outputs.
This requires ongoing, real-time quality management rather than periodic or reactive responses.
Why C is Correct: According to ISACA AAIR data quality guidance, implementing continuous real-time QA processes is the most effective approach for managing variable-quality multi-source inputs. Real-time QA identifies and addresses quality issues as data enters the system-before they contaminate model inputs and outputs. This prevents quality problems from accumulating and ensures the model consistently receives the highest-quality available data.
Why A is Wrong: Synthetic data augmentation is useful for addressing data scarcity but does not resolve accuracy and completeness issues in existing real-world sources. Generating synthetic data alongside poor- quality real data does not improve the real data.
Why B is Wrong: Post-implementation assessments are reactive-they identify problems after they have already affected model behavior and potentially produced harmful outputs. Prevention through real-time QA is superior to post-hoc remediation.
Why D is Wrong: Fine-tuning model parameters can improve robustness to input variation but does not address underlying data quality problems. Models trained to tolerate poor data may produce less reliable outputs than models receiving consistently high-quality data.
NEW QUESTION # 107
Which of the following BEST enables an organization adopting AI solutions to foster an ethical and risk- aware culture?
Answer: C
Explanation:
Organizational culture is primarily shaped by leadership behavior and tone at the top. In AI governance, an ethical culture cannot be mandated through documentation alone-it must be demonstrated through the actions and values of organizational leaders.
Why D is Correct: The ISACA AAIR Study Guide emphasizes that tone at the top is the most powerful driver of ethical culture. When leaders consistently model ethical behavior in AI development and usage, they create a normative environment where employees internalize values rather than merely complying with rules. This authentic leadership approach produces sustainable cultural change.
Why A is Wrong: Checklists are compliance tools that address process adherence, not cultural transformation.
A checklist culture can produce box-ticking behavior without genuine ethical commitment.
Why B is Wrong: Conference participation raises awareness but has minimal impact on day-to-day organizational behavior. External networking does not directly shape internal culture.
Why C is Wrong: Disciplinary actions represent reactive compliance enforcement. While necessary, punitive measures create a compliance-driven rather than values-driven culture, which is less robust and sustainable.
NEW QUESTION # 108
Which of the following BEST mitigates the risk of misaligned return on investment (ROI) in AI initiatives?
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. Mapping AI project outcomes to enterprise performance metrics ties investment to measurable organizational value. An inventory improves visibility and workforce metrics measure only one dimension, while a narrow focus on quantifiable benefits can miss strategic value. This makes option C, Mapping AI project outcomes to enterprise performance metrics, 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 # 109
Which of the following is the MOST important reason for a risk practitioner to validate the alignment of a deployed AI model with business requirements?
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. A deployed model must remain aligned with the business requirements for which it was approved. Validation helps detect unintended behavior that may be technically plausible but operationally harmful, inappropriate, or outside the intended use case. This makes option A, To mitigate risk from unintended model behavior, 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 # 110
Which of the following is the MOST important objective when selecting AI controls based on dataset evaluation and validation?
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. Dataset validation controls should protect the fairness and accuracy of model behavior by ensuring data is suitable, representative, and reliable. Real-time recalibration, framework alignment, and explainability monitoring are useful but do not replace this core objective. This makes option C, Preserving model fairness and accuracy, 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 di rectly. 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 # 111
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