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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Implementation, Maintenance, and Decommissioning - AI Data and Asset Management - AI Model Training, Testing, and Validation |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases - AI Trustworthiness, Ethical and Societal Implications - AI Regulatory Compliance and Legal Considerations - AI Ownership, Oversight, and Accountability - AI Policies, Procedures, and Organizational Training |
| Topic 3: AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Response and Mitigation - AI Risk Assurance and Continuous Improvement |
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NEW QUESTION # 153
Which of the following poses the GREATEST challenge when performing root cause analysis for incidents involving AI systems and data?
Answer: D
Explanation:
Root cause analysis for AI incidents requires the ability to trace system behavior back through decision logic, data processing steps, and model internals to identify what caused the incident. AI systems-particularly deep learning models-often operate as black boxes, making this tracing extremely difficult.
Why A is Correct: According to ISACA AAIR incident management guidance, the lack of transparency in AI systems is the greatest root cause analysis challenge. When decision logic cannot be inspected, when data lineage is unclear, or when model internals are opaque, analysts cannot determine why the system behaved as it did. This transparency deficit prevents accurate root cause identification, perpetuates recurrence, and makes it impossible to demonstrate corrective action to regulators.
Why B is Wrong: Unclear system objectives represent a design and governance problem that should be addressed before deployment. While unclear objectives can contribute to incidents, they are typically knowable and addressable. Lack of transparency during an incident is a more immediate analytical barrier.
Why C is Wrong: Automation bias-the tendency to over-trust automated systems-is a human factors risk that affects decision-making during normal operations. While it may contribute to incidents, it is a behavioral phenomenon rather than the primary technical barrier to root cause analysis.
Why D is Wrong: Privacy compliance requirements may restrict access to certain data needed for analysis, creating constraints on investigation. However, these are governance constraints that can often be addressed through appropriate authorization, not fundamental analytical barriers.
NEW QUESTION # 154
Which of the following testing approaches BEST helps to identify risk in an AI-powered customer support solution?
Answer: A
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. Adversarial testing deliberately supplies malformed, misleading, or hostile inputs to reveal failure modes that ordinary accuracy or load testing may miss. This is especially important for customer-facing AI where unexpected inputs can manipulate or degrade outputs. This makes option A, Using adversarial inputs to determine failure modes, 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 # 155
Which of the following is the PRIMARY concern associated with AI-based attacks on systems that store personal data?
Answer: C
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. AI-enabled re-identification is a direct privacy risk because models can combine attributes that appear harmless in isolation and infer an individual identity. This can defeat ordinary masking or pseudonymization and create regulatory exposure even when direct identifiers are absent. This makes option C, AI models may be able to identify combinations of attributes that can be used to re-identify individuals, 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 # 156
Which of the following is the PRIMARY risk reduction benefit of embedding secure coding practices throughout the AI development life cycle?
Answer: C
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. Secure coding throughout the AI development lifecycle reduces exploitable software and integration vulnerabilities before deployment. It does not directly detect model drift, eliminate human review, or guarantee predictive accuracy. This makes option B, Fewer model vulnerabilities that can be exploited by malicious actors, 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 # 157
An electricity provider uses an AI model to forecast and detect outages in power grids. Which of the following is the MOST important consideration to ensure the accuracy of model outputs?
Answer: A
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. Grid-outage prediction depends on current operational conditions, so real-time sensor data is essential to model accuracy.
Access monitoring and geographic processing improve security or resilience, while manual entry cannot provide the same continuous signal. This makes option A, Integration of real-time sensor data, 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 # 158
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