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| Section | Objectives |
|---|---|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| AI Lifecycle Controls | - Controls across AI development lifecycle
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| AI Risk Management | - Risk identification and assessment for AI systems
|
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NEW QUESTION # 32
Which of the following is MOST important to mitigate risk when integrating third-party AI services into an organization's supply chain?
Answer: B
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. Third-party AI integration requires enforceable allocation of responsibility. Contractual accountability for AI controls clarifies which party must implement, maintain, monitor, and remediate controls across the supplier relationship. This makes option A, Assigning contractual accountability for AI controls, 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 # 33
An organization is designing an enterprise dashboard to support governance of its AI program. Which of the following is the risk practitioner's BEST recommendation?
Answer: B
Explanation:
An enterprise AI governance dashboard must provide decision-makers with a comprehensive, integrated view of AI program health across all dimensions-risk, performance, compliance, ethics, and operations.
Fragmenting this view or focusing on narrow metrics produces an incomplete governance picture.
Why B is Correct: The ISACA AAIR governance reporting guidance recommends aggregating diverse metrics from all AI life cycle stages as the best approach for an enterprise governance dashboard. This comprehensive aggregation enables decision-makers to see the full AI risk and performance picture-from data quality in training through deployment performance, bias monitoring, security incidents, and compliance status-in a single, actionable view. This unified perspective supports informed enterprise-level governance decisions.
Why A is Wrong: Uptime and availability metrics are operational infrastructure indicators that represent only one dimension of AI governance. Focusing primarily on availability misses critical governance concerns including model fairness, accuracy, bias, and ethical compliance.
Why C is Wrong: Risk heat maps based solely on training variance are narrow technical performance indicators. A governance dashboard requires breadth across risk types and life cycle stages, not depth on one specific technical metric.
Why D is Wrong: Assigning dashboard responsibility exclusively to IT centralizes governance reporting in one function that may lack visibility into business risk, ethical compliance, and strategic alignment dimensions of AI governance. Enterprise dashboards require cross-functional input and ownership.
NEW QUESTION # 34
An organization depends on automated decision systems for critical operations. Which of the following MOST effectively mitigates prolonged operational risk after system failures?
Answer: B
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. Tabletop disaster recovery exercises test whether recovery roles, procedures, escalation paths, dependencies, and decisions will work during disruption. Restoration of artifacts is necessary after failure, but exercises validate the organization ' s ability to recover before an incident occurs. This makes option C, Conducting tabletop disaster recovery exercises, 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 # 35
Which of the following is the PRIMARY benefit of defining and documenting a RACI matrix for AI solution development and deployment?
Answer: B
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 # 36
A risk practitioner is evaluating AI model cards and documentation prior to deployment. Which of the following represents the GREATEST risk to enterprise AI governance?
Answer: B
Explanation:
AI governance depends on the ability of stakeholders to understand, audit, and oversee AI model decisions.
Explainability is the technical and documentation property that enables this oversight. When model cards fail to adequately document explainability, the entire governance chain is compromised.
Why B is Correct: According to ISACA AAIR, inadequate explainability in model documentation is the greatest governance risk because it prevents risk practitioners, auditors, regulators, and business owners from understanding why a model produces its outputs. Without explainability, discriminatory or erroneous decisions cannot be identified, challenged, or corrected. This undermines accountability, compliance, and responsible AI governance at the enterprise level.
Why A is Wrong: Regulatory filing delays represent a compliance timing issue that can be remediated. While risky, they do not fundamentally compromise the governance capability of understanding and overseeing AI behavior.
Why C is Wrong: Decentralized version control creates configuration management challenges and audit trail gaps. These are significant but can be remediated through governance process improvements. Explainability gaps affect the underlying ability to govern the model itself.
Why D is Wrong: Overly detailed technical specifications represent a documentation quality issue that may reduce usability but does not create a governance risk. Excessive detail is easily distilled; absent explainability cannot be reconstructed after the fact.
NEW QUESTION # 37
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