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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI governance communication and reporting - AI risk assessment and treatment strategies - Enterprise AI risk program design - AI risk monitoring and continuous improvement |
| AI Life Cycle Risk Management | - AI model and data risk identification - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks | |
| AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases |
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NEW QUESTION # 172
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: C
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 # 173
An organization deploys an AI credit scoring model trained on historical financial data that underrepresents certain demographic groups. Which of the following is the risk practitioner's BEST recommendation to mitigate this risk?
Answer: B
Explanation:
Bias in AI models often originates from training data that does not represent the full population the model will serve. Underrepresentation of demographic groups in training data causes the model to perform poorly for those groups, producing discriminatory outcomes in high-stakes decisions like credit scoring.
Why B is Correct: The ISACA AAIR bias and fairness guidance identifies expanding training data coverage as the most effective mitigation for representation bias. Defining specific inclusivity goals ensures the data expansion targets the identified gaps, while broadening data sources introduces representative examples from underrepresented groups. This addresses the root cause-training data deficiency-rather than symptoms.
Why A is Wrong: Model drift reporting detects changes in model behavior over time but does not address existing representational bias embedded in the current model. Monitoring an already-biased model cannot remediate the bias.
Why C is Wrong: Notifying stakeholders of potential inaccuracy is a transparency measure but does not reduce harm to affected individuals. Disclosure of bias without remediation is insufficient under anti- discrimination regulations.
Why D is Wrong: Unsupervised learning can identify hidden patterns but cannot introduce the missing representative data needed to train an unbiased model. Discovering discriminatory patterns in existing data does not resolve the underlying data coverage gap.
NEW QUESTION # 174
Which of the following is the GREATEST organizational risk when AI performance alerts are not escalated to decision-makers for review and decisioning?
Answer: B
NEW QUESTION # 175
Which of the following is the PRIMARY reason to lower AI model temperature?
Answer: C
Explanation:
Temperature is a hyperparameter in language model generation that controls output randomness. Lower temperatures make the model more deterministic-concentrating probability mass on the most likely tokens and producing more consistent, predictable outputs. Higher temperatures introduce more randomness and diversity.
Why B is Correct: According to ISACA AAIR model configuration guidance, lowering model temperature is primarily used to enhance consistency and accuracy of outputs. In production applications requiring reliable, reproducible responses-such as customer service, compliance reporting, or technical documentation-lower temperature ensures the model consistently generates the most appropriate response based on its learned knowledge, reducing variability and improving output quality.
Why A is Wrong: Temperature adjustment does not directly mitigate bias. Bias in AI models is a function of training data and model architecture, not output randomness. A biased model at low temperature will consistently generate biased outputs; lowering temperature may actually make bias more persistent by reducing variation.
Why C is Wrong: Diversifying ideas and recommendations is achieved by increasing temperature, not lowering it. Higher temperature is used for creative tasks where variety is valuable; lower temperature is used for tasks requiring precision and consistency.
Why D is Wrong: Model temperature has no direct relationship to computational energy consumption. Energy use is primarily driven by model size, computation requirements, and inference frequency-not the temperature parameter.
NEW QUESTION # 176
Which of the following is the PRIMARY reason to include contractual requirements for model updates and disclosures from third-party AI suppliers?
Answer: D
Explanation:
Third-party AI suppliers introduce significant risk through model updates, changes in training data, and modifications to system behavior. Contractual disclosure requirements ensure the acquiring organization can maintain active risk oversight despite not controlling the vendor's development processes.
Why B is Correct: The ISACA AAIR framework emphasizes that third-party AI contracts must protect against harms arising from undisclosed changes. When vendors make silent updates to models, the acquiring organization cannot assess new risks before they affect users, decisions, or regulated outcomes. Timely disclosure requirements enable proactive risk detection and mitigation before individuals are harmed.
Why A is Wrong: Availability guarantees are service-level concerns addressed by SLA provisions. While important operationally, they do not address the risk management imperative of understanding what changes have been made to AI models.
Why C is Wrong: Internal trust-building is a change management consideration, not the primary purpose of contractual disclosure requirements. Contracts address risk obligations, not organizational confidence.
Why D is Wrong: Vendor staff access to sensitive datasets is a data access and privacy concern addressed through data processing agreements and access controls, not model update disclosure requirements.
NEW QUESTION # 177
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