Itcertkr사이트에서 제공하는ISACA 인증AAIR 덤프의 일부 문제와 답을 체험해보세요. 우리 Itcertkr의 를ISACA 인증AAIR 덤프공부자료를 선택해주신다면 우리는 최선을 다하여 여러분이 꼭 한번에 시험을 패스할 수 있도록 도와드리겠습니다.만약 여러분이 우리의 인증시험 덤프를 보시고 시험이랑 틀려서 패스를 하지 못하였다면 우리는 무조건 덤프비용 전부를 환불해드릴것입니다. Itcertkr제품으로 자격증을 정복합시다!
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment |
| Topic 2: AI Life Cycle Risk Management | - AI model and data risk identification - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation | |
| Topic 3: AI Risk Program Management | 42% | - AI governance communication and reporting - AI risk monitoring and continuous improvement - AI risk assessment and treatment strategies - Enterprise AI risk program design |
Itcertkr의 ISACA인증 AAIR덤프를 구매하여 공부한지 일주일만에 바로 시험을 보았는데 고득점으로 시험을 패스했습니다.이는Itcertkr의 ISACA인증 AAIR덤프를 구매한 분이 전해온 희소식입니다. 다른 자료 필요없이 단지 저희ISACA인증 AAIR덤프로 이렇게 어려운 시험을 일주일만에 패스하고 자격증을 취득할수 있습니다.덤프가격도 다른 사이트보다 만만하여 부담없이 덤프마련이 가능합니다.구매전 무료샘플을 다운받아 보시면 믿음을 느낄것입니다.
질문 # 157
Which of the following is the PRIMARY benefit of aligning AI risk management with existing organizational governance frameworks?
정답：D
설명：
Organizational governance frameworks provide the structures, processes, and oversight mechanisms through which enterprises manage their activities and risks. Aligning AI risk management with these frameworks ensures AI activities receive the same level of strategic oversight as other organizational functions.
Why C is Correct: The ISACA AAIR curriculum identifies enterprise-level oversight and strategic alignment as the primary benefit of governance framework integration. When AI risk management operates within established governance structures, AI decisions are subject to the same approval authorities, risk escalation pathways, and strategic alignment checks that govern all major organizational decisions. This produces coherent, enterprise-aware AI governance.
Why A is Wrong: Role development and responsibility clarification are governance activities that may result from alignment, but they represent structural outputs rather than the primary benefit. The benefit is the oversight quality, not the organizational structure itself.
Why B is Wrong: Expediting compliance approvals is an efficiency benefit that may arise from better- organized governance. However, speed of approval is not the primary purpose of framework alignment-the purpose is quality and consistency of oversight.
Why D is Wrong: Standardizing acquisition processes is a procurement function benefit. While governance alignment may improve procurement consistency, standardization is a narrow operational benefit compared to the strategic oversight value of full governance integration.
질문 # 158
Which of the following AI system considerations BEST mitigates risk associated with model drift?
정답：C
설명：
Model drift occurs when the statistical relationship between model inputs and outputs changes over time, causing previously accurate predictions to become less reliable. Regular retraining with updated, relevant data recalibrates the model to current real-world patterns.
Why A is Correct: According to ISACA AAIR model maintenance guidance, regular retraining with new relevant datasets is the most direct mitigation for model drift. By periodically retraining on current data, the model learns the latest patterns and relationships-counteracting the drift that accumulates as real-world conditions diverge from the original training data. This is the standard industry practice for maintaining production AI models in dynamic environments.
Why B is Wrong: Restricting automated data validation to low-risk models creates a governance double standard that leaves high-risk models more vulnerable. If anything, high-risk models require more rigorous automated validation, not less. This approach increases rather than mitigates drift risk for critical applications.
Why C is Wrong: Maintaining existing dataset variance during preprocessing preserves statistical characteristics from a historical snapshot. If drift has occurred in real-world data, deliberately maintaining old variance levels prevents the model from adapting to new conditions.
Why D is Wrong: Role-based access controls protect model parameters and data from unauthorized modification. While important for security, access controls do not address model drift, which is driven by changing real-world conditions rather than unauthorized changes.
질문 # 159
Which AI security by design option BEST mitigates targeted model poisoning and supply chain tampering?
정답：A
설명：
Model poisoning attacks target the training data or model parameters to degrade performance or introduce malicious behavior. Supply chain tampering introduces compromised components at vendor or integration stages. Security by design principles require embedding defenses against these threats from the earliest design stages.
Why C is Correct: According to ISACA AAIR security by design guidance, adversarial resilience and data integrity controls address both model poisoning and supply chain tampering at their root. Adversarial resilience training prepares the model to resist maliciously crafted inputs. Data integrity controls- cryptographic signing, provenance tracking, integrity verification-detect tampering in training data and model artifacts across the supply chain. Together, these form the most comprehensive defense against both attack categories.
Why A is Wrong: Data refreshes with checksums detect post-hoc data corruption but do not build adversarial resilience into the model itself. Checksums verify file integrity but cannot prevent poisoning attacks that maintain file integrity while altering data content.
Why B is Wrong: Frequent retraining and bias monitoring address performance drift and fairness but do not specifically protect against deliberate tampering. A retrained model may still be trained on poisoned data if integrity controls are absent.
Why D is Wrong: Data tokenization protects sensitive field values from unauthorized access (a privacy control) but does not address model poisoning or supply chain tampering, which can occur without accessing or exposing the sensitive field values themselves.
질문 # 160
Which of the following is the MOST important consideration when assessing risk for an AI system provided by a third party, as opposed to a non-AI related system?
정답：B
설명：
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. Third-party AI requires greater continuous monitoring because models, data, dependencies, and provider behavior can change over time. Traditional onboarding assessments alone may not detect drift, updates, or newly introduced AI-specific risk. This makes option C, Greater emphasis on continuous monitoring due to the dynamically evolving nature of AI models, 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.
질문 # 161
Which of the following is the PRIMARY purpose of applying thorough cleansing and normalization to AI training data?
정답：C
설명：
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.
질문 # 162
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Itcertkr사이트에서 제공하는ISACA 인증AAIR 덤프의 일부 문제와 답을 체험해보세요. 우리 Itcertkr의 를ISACA 인증AAIR 덤프공부자료를 선택해주신다면 우리는 최선을 다하여 여러분이 꼭 한번에 시험을 패스할 수 있도록 도와드리겠습니다.만약 여러분이 우리의 인증시험 덤프를 보시고 시험이랑 틀려서 패스를 하지 못하였다면 우리는 무조건 덤프비용 전부를 환불해드릴것입니다. Itcertkr제품으로 자격증을 정복합시다!
AAIR퍼펙트 덤프 최신 데모문제: https://www.itcertkr.com/AAIR_exam.html