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| Section | Objectives |
|---|---|
| Topic 1: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 2: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 3: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 4: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Topic 5: AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
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NEW QUESTION # 50
Which of the following is MOST important to evaluate when selecting a vendor for a third-party large language model (LLM)?
Answer: B
Explanation:
Third-party LLMs process organizational data-including sensitive and proprietary information-during both training and inference. The vendor's data handling practices determine whether the organization's data remains private, secure, and compliant with legal obligations.
Why D is Correct: According to ISACA AAIR third-party risk guidance, data handling practices are the most critical evaluation criterion for AI vendors. How the vendor uses input data-whether for model training, analytics, or retention-directly determines data privacy risk, intellectual property exposure, and regulatory compliance. Vendors who train on customer input data without restriction create significant privacy and confidentiality risks.
Why A is Wrong: SLA alignment with corporate strategy addresses availability and performance obligations.
While important, these commercial terms do not address the fundamental data risk created by vendor data handling practices.
Why B is Wrong: ML method selection reflects technical sophistication but does not determine data risk. The risk profile is driven by data governance, not algorithmic choice.
Why C is Wrong: Subscription models represent commercial and procurement considerations. Pricing structure has no bearing on data privacy risk or the organization's risk exposure from vendor data practices.
NEW QUESTION # 51
A healthcare organization plans to use synthetic records in medical research to help protect patient privacy.
Which of the following is the GREATEST risk associated with using synthetic data to train AI models?
Answer: D
Explanation:
Synthetic data is generated algorithmically to resemble real data while protecting individual privacy.
However, synthetic data generation processes may not perfectly capture the full statistical diversity of real- world populations-particularly rare conditions, edge cases, and underrepresented demographic groups.
Why A is Correct: According to ISACA AAIR data quality guidance for AI, the greatest risk of training on synthetic data is that it may not reflect real-world diversity. In healthcare, this is particularly consequential because AI models trained on non-diverse synthetic data may perform poorly for patient populations not well- represented in the original real data-potentially producing inaccurate diagnoses or treatment recommendations for vulnerable groups, perpetuating health inequities.
Why B is Wrong: While reduced diversity could contribute to increased false negatives in some scenarios, this is a specific manifestation of the broader diversity problem. The root cause-lack of real-world representativeness-is the more fundamental and comprehensive risk.
Why C is Wrong: Regulatory noncompliance from synthetic data use depends on jurisdiction-specific requirements. Many regulations explicitly encourage synthetic data to protect privacy. While compliance must be verified, it is not the greatest inherent risk of synthetic data quality.
Why D is Wrong: Synthetic data generation occurs in controlled internal environments and is not inherently more susceptible to data poisoning than other data types. Poisoning risk is a function of data pipeline controls, not whether data is synthetic or real.
NEW QUESTION # 52
An organization seeks to implement a new AI system that uses customer information to create targeted product recommendations. Which of the following is the MOST important consideration to ensure the system complies with regulatory requirements?
Answer: B
Explanation:
Privacy and data protection regulations worldwide-including GDPR, CCPA, and sector-specific laws- impose strict requirements on the collection, use, and processing of personal information. Customer data used for AI systems must be obtained through lawful means with appropriate consent for the specific processing purpose.
Why A is Correct: According to ISACA AAIR guidance on regulatory compliance, the legal basis for processing personal data is the foundational requirement. An AI system built on data collected without proper consent or legal authorization exposes the organization to regulatory penalties, reputational damage, and forced shutdown of the system. Consent must be specific to the AI use case, not merely generic data collection consent.
Why B is Wrong: Backup and storage protocols address data security and resilience, which are compliance requirements but secondary to the lawfulness of data collection. Securely storing improperly obtained data does not cure the regulatory violation.
Why C is Wrong: Human review of recommendations is a governance safeguard for accuracy and fairness, not a regulatory compliance requirement for data collection. Many regulations do not require human review of recommendation systems.
Why D is Wrong: Supervised learning is a modeling technique that does not address regulatory compliance regarding data sourcing. The training methodology is irrelevant to whether the underlying data was legally obtained.
NEW QUESTION # 53
Which of the following is the PRIMARY benefit of aligning AI risk management with existing organizational governance frameworks?
Answer: D
Explanation:
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.
NEW QUESTION # 54
Which of the following should be the PRIMARY consideration when determining the priority for restoration of AI systems following a model exfiltration attack?
Answer: D
Explanation:
Following a model exfiltration attack, multiple AI systems may require restoration. Prioritization must be based on objective criteria that reflect the potential business impact of continued unavailability. Systems supporting critical business functions must be restored before those supporting non-critical functions.
Why A is Correct: According to ISACA AAIR business continuity guidance for AI, the primary criterion for restoration priority is the AI system's criticality to business requirements. Systems that support mission- critical functions-patient care, financial transaction processing, safety operations-represent the highest restoration priority because their unavailability causes the greatest operational harm. This risk-based prioritization framework is consistent with standard business continuity management principles applied to the AI context.
Why B is Wrong: Team member expertise affects restoration capacity and speed but should not drive prioritization decisions. Priority is determined by business impact, not by where the team has the most technical capability. Resource allocation follows priority, not the reverse.
Why C is Wrong: Vulnerability testing and patch costs are operational considerations that may influence restoration timelines but should not override business criticality in determining priority. Cost-based prioritization could lead to restoring cheaper but less critical systems first.
Why D is Wrong: Dataset availability affects the feasibility and timeline of model retraining but is a logistical consideration rather than the primary basis for restoration priority. Critical systems should be prioritized even if their restoration is technically more complex.
NEW QUESTION # 55
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