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| Section | Objectives |
|---|---|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| AI Risk Management | - Risk identification and assessment for AI systems
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| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
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NEW QUESTION # 81
Which of the following is the PRIMARY purpose of applying thorough cleansing and normalization to AI training data?
Answer: D
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. Cleansing and normalization reduce inconsistent, erroneous, or statistically distorted training data that can produce biased or inaccurate model behavior. Their primary value is improving trustworthy inputs rather than lowering compute cost or satisfying privacy law by themselves. This makes option C, Addressing statistical distortions in training data that could cause model bias, 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 # 82
After which of the following events is it MOST important to update risk ratings?
Answer: D
Explanation:
Risk ratings must be maintained as current assessments of organizational risk exposure. Events that materially change the risk profile-particularly those indicating active harm or regulatory violations-require immediate risk rating updates to ensure governance responses are calibrated to the current risk reality.
Why A is Correct: According to ISACA AAIR risk monitoring and review guidance, the discovery of discriminatory outputs from an AI system represents a material change in risk exposure that requires immediate risk rating updates. Discriminatory outputs indicate active harm to individuals, regulatory violations, and significant legal and reputational exposure. This event fundamentally changes the risk profile from a potential to an actual harm, requiring escalated risk ratings and treatment responses.
Why B is Wrong: Adding new monitoring metrics improves risk detection capability but does not change the underlying risk levels. New metrics may subsequently detect risks requiring rating updates, but their addition alone is an operational change, not a risk level change.
Why C is Wrong: Vulnerability patch deployment reduces risk by closing specific security gaps, which may lower risk ratings but is less urgent than updating ratings to reflect active harm discovery. Patching is a remediation activity; discriminatory outputs represent ongoing harm requiring immediate escalation.
Why D is Wrong: Creating an oversight committee improves governance capability but does not change the risk profile of AI systems. Governance structure changes affect the organization's ability to manage risk; they do not affect the risk levels themselves.
NEW QUESTION # 83
An election oversight body is considering the use of AI to identify irregularities in voting patterns. Which of the following is the MOST important risk to evaluate?
Answer: B
Explanation:
AI systems trained on historical data inherit the biases, patterns, and structural inequities embedded in that data. In electoral contexts, historical voting patterns may reflect systemic disenfranchisement, gerrymandering, or demographic manipulation-biases that an AI system could amplify and legitimize through its outputs.
Why B is Correct: According to ISACA AAIR bias and fairness guidance applied to high-stakes public sector AI, the amplification of historical data biases poses the greatest risk in electoral irregularity detection. If the AI system treats historically suppressed voting patterns as the normal baseline, it may flag legitimate turnout increases in previously underrepresented communities as irregularities-producing discriminatory, biased outputs with severe democratic consequences.
Why A is Wrong: Voter location identification is a privacy concern but represents a specific data element risk.
Comprehensive privacy controls can mitigate location exposure without resolving the systemic bias risk.
Why C is Wrong: Contextual drift-the model performing differently in new electoral contexts than in training contexts-is a technical risk that is relevant but addressable through validation testing. Bias amplification is a more fundamental concern embedded in the historical data itself.
Why D is Wrong: Political distrust of AI represents a stakeholder acceptance challenge. While significant for implementation success, it is a communication and change management concern rather than the primary technical and ethical risk from the AI system itself.
NEW QUESTION # 84
An organization formally accepts residual risk for a low-impact AI capability after confirming it falls within the enterprise risk tolerance.
What is the primary benefit of this risk acceptance action?
Answer: D
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. When residual risk is within tolerance and affects non-critical functions, formal acceptance is proportionate and allows scarce mitigation resources to be directed toward higher-impact exposures.
Acceptance must still be documented and monitored rather than treated as inaction. This makes option A, It enables the allocation of mitigation resources to more critical AI-specific issues, 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 # 85
A risk practitioner assesses an AI model used for predictive diagnostics and finds that the system lacks automated update mechanisms. Which of the following is the GREATEST risk?
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. A predictive diagnostic model that cannot update may continue using assumptions that no longer reflect current conditions, compromising accuracy as the environment changes. Human effort is secondary to the risk of incorrect predictions based on stale relationships. This makes option D, Compromise of predictive accuracy due to outdated model assumptions, 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 # 86
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