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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning |
| Topic 2: AI Risk Program Management | 42% | - AI Risk Assurance and Continuous Improvement - AI Risk Identification and Assessment - AI Risk Response and Mitigation - AI Risk Monitoring and Reporting |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Policies, Procedures, and Organizational Training - AI Trustworthiness, Ethical and Societal Implications - AI Ownership, Oversight, and Accountability - AI Regulatory Compliance and Legal Considerations - AI Organizational Processes and Alignment |
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NEW QUESTION # 91
Which of the following is the BEST course of action to mitigate risk during model selection of supervised or unsupervised algorithms?
Answer: D
Explanation:
Algorithm selection is a foundational risk management decision in AI development. The wrong algorithm for a given use case can produce inaccurate, unreliable, or harmful outputs regardless of the quality of training data or computational resources applied.
Why D is Correct: The ISACA AAIR model development guidance identifies use case alignment as the most critical algorithm selection criterion. Supervised and unsupervised learning are suited to fundamentally different problem types-supervised learning requires labeled training data and learns mappings to known outputs; unsupervised learning discovers patterns in unlabeled data. Selecting algorithms whose capabilities match the use case's structure and objectives prevents systematic performance failures and misapplied AI.
Why A is Wrong: Generalization capability is an important model quality criterion but represents one of many algorithmic properties. Strong generalization on the wrong problem type still produces poor results. Use case alignment precedes generalization as a selection criterion.
Why B is Wrong: Requiring supervised learning for all training projects is an inappropriate blanket policy.
Many valuable use cases-anomaly detection, customer segmentation, exploratory analytics-are better served by unsupervised approaches. Mandating supervised learning prevents optimal use case matching.
Why C is Wrong: Computational cost is a resource management consideration. Optimizing for cost at the expense of use case fit risks deploying inappropriate models that produce unreliable outputs, creating far greater costs through remediation or harm.
NEW QUESTION # 92
An organization integrates multiple AI services using APIs to enhance a customer support chatbot. Which of the following is the GREATEST risk?
Answer: A
Explanation:
API integration with external AI services creates data transmission pathways between the organization and external systems. Customer support contexts involve sensitive personal data-account information, contact details, inquiry content-that may be transmitted through these API connections.
Why B is Correct: The ISACA AAIR security and privacy guidance identifies unauthorized disclosure of sensitive data through insecure API connections as the greatest risk in multi-service AI integration. APIs can be vulnerable to interception, inadequate authentication, or misconfiguration. In a customer support context, exposure of personal data via API vulnerabilities creates privacy violations, regulatory liability, and reputational harm-all more severe than the other listed concerns.
Why A is Wrong: Bias and inaccuracy in chatbot responses are real quality risks but represent service quality issues rather than security or privacy breaches. Inaccurate responses are visible and correctable; data breaches may go undetected.
Why C is Wrong: Customer dissatisfaction from operational delays is a service quality and business risk. It is a manageable consequence of performance issues rather than the greatest risk from API-based AI integration.
Why D is Wrong: Insufficient training datasets affect model quality but are a development concern addressed during the model selection phase. They do not represent the primary operational risk of deploying multi- service API integrations in production.
NEW QUESTION # 93
To reinforce organization-wide ethical norms and risk recognition, which of the following is MOST important to integrate into AI user training?
Answer: B
Explanation:
Effective AI user training must go beyond policy acknowledgment and compliance instruction to equip employees with the practical skills needed to identify ethical risks and report them appropriately. This builds an active risk-aware workforce.
Why B is Correct: The ISACA AAIR framework identifies that training on ethical risk indicators and reporting mechanisms directly reinforces ethical norms by enabling employees to recognize real-world signs of AI misuse, bias, or harmful outputs. When staff can identify specific risk signals and know how to escalate them, the organization builds a proactive risk culture grounded in practical ethical literacy.
Why A is Wrong: Acceptable use policy acknowledgment is a compliance activity, not a culture-building measure. Acknowledging a document does not ensure employees understand how to apply ethical principles in practice.
Why C is Wrong: Cyber threat identification addresses security risk, which is narrower than the full scope of ethical AI risk. Security training does not develop ethical judgment regarding fairness, bias, or societal impact.
Why D is Wrong: Regulatory compliance checklists address legal obligations but do not develop the ethical reasoning and risk recognition skills needed to reinforce organizational norms.
NEW QUESTION # 94
An organization depends on multiple external suppliers for AI models and training datasets. Which of the following is MOST important to have in place in order to reduce supply chain risk?
Answer: C
Explanation:
AI supply chain risk arises when external models or datasets are tampered with, have undisclosed characteristics, or cannot be traced to trusted origins. End-to-end provenance and audit trails address these risks by enabling verification of integrity and origin at every stage of the supply chain.
Why A is Correct: According to ISACA AAIR supply chain risk management guidance, verifiable provenance and audit trails are the most important supply chain protection mechanism. Provenance documentation traces the origin, handling, and transformation history of every externally sourced AI artifact- enabling the organization to verify that models and datasets have not been tampered with, that data sources are legitimate, and that the supply chain has not been compromised. Without provenance, organizations cannot distinguish trustworthy from compromised artifacts.
Why B is Wrong: Indemnity clauses assign financial liability after harm occurs. They provide legal recourse but do not prevent supply chain attacks or help the organization verify artifact integrity before deployment.
Why C is Wrong: Training method documentation provides useful technical context but does not verify that the actual artifacts delivered match the documentation. Documentation can be falsified; provenance verification with cryptographic integrity checks cannot.
Why D is Wrong: A vendor risk manager provides governance oversight and relationship management. While important for managing vendor relationships, a single contact point does not substitute for technical provenance verification of every artifact in the supply chain.
NEW QUESTION # 95
Which of the following poses the GREATEST risk during the development phase of an AI system?
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. Data poisoning can corrupt the learning process before deployment and embed harmful or biased behavior into the resulting model. Because it attacks training-data integrity directly, inadequate poisoning mitigation is a major development-phase risk. This makes option A, Inadequate mitigation of data poisoning attacks, 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 # 96
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