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| Section | Objectives |
|---|---|
| Topic 1: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 2: AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Topic 3: AI Risk Management | - Risk identification and assessment for AI systems
|
| Topic 4: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| Topic 5: AI Lifecycle Controls | - Controls across AI development lifecycle
|
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NEW QUESTION # 134
Which of the following is MOST likely to be found in a risk mitigation plan for AI systems?
Answer: C
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. A risk mitigation plan should contain concrete measures that reduce identified AI risks, such as controls for data poisoning and algorithmic bias. Strategic objectives and vendor-selection criteria are governance inputs, not the actual treatment measures for the identified risk. This makes option C, Measures to address issues such as data poisoning and algorithmic 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.
NEW QUESTION # 135
Which of the following is the PRIMARY benefit of using AI to assist with risk scenario identification?
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. AI assists risk scenario identification primarily through rapid analysis and pattern recognition across large volumes of information. Simulation and scoring are downstream capabilities, while pattern detection expands the practitioner ' s ability to identify emerging scenarios. This makes option C, Improved real-time analysis and pattern recognition, 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 # 136
Which of the following is the MOST important consideration when an organization is decommissioning an AI system and must manage its associated datasets?
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.
Decommissioning requires formal retention rules for datasets, models, logs, and supporting artifacts so information is kept only as long as necessary and then disposed of appropriately. Encryption protects retained data but does not decide whether it should still exist. This makes option C, Retention policies for storage of system components and artifacts, 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 # 137
A risk practitioner assesses a new AI system and determines that the risk is within the organization ' s risk tolerance. Which of the following is the BEST recommendation to ensure system controls remain effective over time?
Answer: C
NEW QUESTION # 138
Which of the following AI system considerations BEST mitigates risk associated with model drift?
Answer: C
Explanation:
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.
NEW QUESTION # 139
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