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| Section | Objectives |
|---|---|
| Topic 1: AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Topic 2: AI Risk Management | - Risk identification and assessment for AI systems
|
| Topic 3: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| Topic 4: AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Topic 5: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
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NEW QUESTION # 72
Which of the following is the PRIMARY benefit of defining and documenting a RACI matrix for AI solution development and deployment?
Answer: D
Explanation:
A RACI (Responsible, Accountable, Consulted, Informed) matrix is a governance tool that explicitly maps roles and decision authority across project activities. For AI systems, RACI frameworks ensure that accountability for decisions, outputs, and risk management is clearly defined and documented.
Why D is Correct: The ISACA AAIR curriculum identifies the RACI matrix as a foundational accountability instrument. Its primary benefit is establishing unambiguous responsibility and decision authority, which is essential for AI governance where multiple stakeholders-technical teams, business owners, risk practitioners, compliance officers-must work together with clear lanes of authority. This clarity prevents accountability gaps and ensures risk management actions are owned.
Why A is Wrong: Facilitating collaboration is a secondary benefit. While RACI does support cross-functional coordination, collaboration enablement is not its defining purpose. Collaboration can occur without a RACI through other mechanisms.
Why B is Wrong: Consolidating governance authority in senior leadership describes centralization, which is not the purpose of RACI. In fact, RACI typically distributes responsibility across multiple levels rather than consolidating it.
Why C is Wrong: Strengthening technical development governance is an application of the RACI, not its primary benefit. The RACI benefit is accountability clarity, which then supports technical and architectural governance.
NEW QUESTION # 73
Which of the following is the PRIMARY risk associated with the use of unsupervised learning methods to train AI models?
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.
Unsupervised learning operates without predefined labels, making discovered patterns harder to validate and explain against known targets. This creates particular assurance challenges around interpretation, labeling, and explainability compared with supervised classification. This makes option D, Lack of labeling and explainability, 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 # 74
An organization plans to deploy AI for customer service automation. Which of the following governance approaches BEST aligns culture and risk tolerance?
Answer: A
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. Governance should scale controls to the impact of the customer-service AI system and involve business stakeholders so the control environment reflects risk tolerance and gains operational support. Monitoring alone does not create cultural alignment. This makes option C, Scale controls to AI system impact and involve business stakeholders to obtain buy-in, 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 # 75
Which of the following is the PRIMARY benefit of adopting an AI governance framework?
Answer: B
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. An AI governance framework primarily creates structured enterprise oversight, accountability, policy alignment, monitoring, and compliance. Resource allocation and technical standardization may improve, but they are secondary outcomes rather than the central governance benefit. This makes option A, Structured enterprise AI risk oversight and compliance, 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 # 76
An organization uses an AI model that learns from live data streams. Which of the following is the BEST course of action to manage the risk of an adaptive model?
Answer: B
Explanation:
AI models that learn from live data streams continuously update their parameters based on incoming data.
This creates two specific risks: the model's behavior may drift from its validated state as data patterns change (data drift), and adversaries may deliberately introduce malicious data to manipulate the model's learning (data poisoning).
Why D is Correct: According to ISACA AAIR adaptive model risk guidance, implementing automated monitoring for both data drift and data poisoning is the most comprehensive response to live-learning model risks. Automated monitoring operates continuously at the speed of the data stream, detecting statistical changes in input distributions (drift signals) and anomalous data patterns (poisoning signals) in real time- enabling timely intervention before either risk materializes into harmful behavior.
Why A is Wrong: Defense-in-depth for model access controls who can interact with the model but does not address risks arising from the data the model learns from. Access controls are necessary but insufficient for managing adaptive learning risks.
Why B is Wrong: Restricting data sources reduces learning breadth, potentially undermining the model's adaptive capability that creates its value. Periodic inspections are too infrequent for live-learning systems where risks can emerge between inspection cycles.
Why C is Wrong: Dynamic performance thresholds detect output degradation after drift has occurred. While useful as a safety net, this reactive monitoring does not prevent drift or detect poisoning early enough for the live-learning risk context.
NEW QUESTION # 77
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