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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 Risk Management | - Risk identification and assessment for AI systems
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| Topic 3: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Topic 4: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 5: AI Lifecycle Controls | - Controls across AI development lifecycle
|
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NEW QUESTION # 16
An organization uses an AI model that learns from live data streams. Which of the following is the BEST course of action to manage the risk of an adaptive model?
Answer: D
Explanation:
AI models that learn from live data streams continuously update their parameters based on incoming data.
This creates two specific risks: the model's behavior may drift from its validated state as data patterns change (data drift), and adversaries may deliberately introduce malicious data to manipulate the model's learning (data poisoning).
Why D is Correct: According to ISACA AAIR adaptive model risk guidance, implementing automated monitoring for both data drift and data poisoning is the most comprehensive response to live-learning model risks. Automated monitoring operates continuously at the speed of the data stream, detecting statistical changes in input distributions (drift signals) and anomalous data patterns (poisoning signals) in real time- enabling timely intervention before either risk materializes into harmful behavior.
Why A is Wrong: Defense-in-depth for model access controls who can interact with the model but does not address risks arising from the data the model learns from. Access controls are necessary but insufficient for managing adaptive learning risks.
Why B is Wrong: Restricting data sources reduces learning breadth, potentially undermining the model's adaptive capability that creates its value. Periodic inspections are too infrequent for live-learning systems where risks can emerge between inspection cycles.
Why C is Wrong: Dynamic performance thresholds detect output degradation after drift has occurred. While useful as a safety net, this reactive monitoring does not prevent drift or detect poisoning early enough for the live-learning risk context.
NEW QUESTION # 17
Which of the following is MOST important to evaluate when selecting a vendor for a third-party large language model (LLM)?
Answer: C
Explanation:
Third-party LLMs process organizational data-including sensitive and proprietary information-during both training and inference. The vendor's data handling practices determine whether the organization's data remains private, secure, and compliant with legal obligations.
Why D is Correct: According to ISACA AAIR third-party risk guidance, data handling practices are the most critical evaluation criterion for AI vendors. How the vendor uses input data-whether for model training, analytics, or retention-directly determines data privacy risk, intellectual property exposure, and regulatory compliance. Vendors who train on customer input data without restriction create significant privacy and confidentiality risks.
Why A is Wrong: SLA alignment with corporate strategy addresses availability and performance obligations.
While important, these commercial terms do not address the fundamental data risk created by vendor data handling practices.
Why B is Wrong: ML method selection reflects technical sophistication but does not determine data risk. The risk profile is driven by data governance, not algorithmic choice.
Why C is Wrong: Subscription models represent commercial and procurement considerations. Pricing structure has no bearing on data privacy risk or the organization's risk exposure from vendor data practices.
NEW QUESTION # 18
An organization has deployed an AI-powered customer service chatbot. Which of the following BEST helps to ensure the chatbot maintains high accuracy in interpreting and answering customer inquiries?
Answer: D
Explanation:
Chatbot accuracy in customer service depends on correctly identifying customer intent and generating appropriate responses. Both intent classification accuracy and training data quality directly determine chatbot performance over time.
Why D is Correct: According to ISACA AAIR model performance management guidance, measuring intent- classification error rates provides precise diagnostic information about where the chatbot misunderstands customer inquiries, while refining training datasets based on those errors continuously improves classification accuracy. This closed-loop approach-measure specific errors, improve the underlying data that drives them- is the most effective mechanism for sustained high accuracy.
Why A is Wrong: Increasing model temperature increases output randomness and diversity, which is counterproductive for accuracy in customer service contexts where consistent, precise answers are required.
Precision and recall provide useful metrics but increased temperature actively undermines accuracy.
Why B is Wrong: Vendor benchmarking compares performance against generic standards. Customer service chatbots must be optimized for the specific organization's terminology, products, and customer base-generic thresholds may not capture the accuracy requirements of a specific deployment.
Why C is Wrong: Explainable AI techniques improve decision transparency but do not directly enhance classification accuracy. Code reviews address software quality, not the model's ability to accurately interpret customer intent.
NEW QUESTION # 19
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 # 20
An organization has deployed generative AI tools broadly but lacks a consistent method to refresh governance policies and controls. Which of the following is the risk practitioner's BEST recommendation?
Answer: D
Explanation:
Generative AI capabilities and the associated risk landscape evolve rapidly. Governance policies and controls must be refreshed through a structured, regular process rather than reactively or only when compliance requirements change.
Why A is Correct: According to ISACA AAIR, establishing a regular review cadence with codified reassessment procedures is the most robust approach because it creates a systematic, predictable process for keeping governance current. By documenting when and how policies will be reviewed-including triggers for ad hoc review (new deployments, incidents, regulatory changes)-the organization ensures governance never stagnates regardless of external pressures.
Why B is Wrong: Regulatory alignment is an important input to governance refresh but represents a reactive, external-trigger approach. Relying primarily on regulatory signals means governance lags behind organizational AI changes not covered by new regulations.
Why C is Wrong: Centralizing authority in executive and technical leadership creates decision bottlenecks and reduces the operational agility needed to keep pace with rapidly evolving AI deployments. Distributed governance with clear escalation paths is more effective.
Why D is Wrong: Annual reviews are too infrequent for generative AI tools, which may see significant capability changes and risk profile shifts multiple times per year. Annual compliance audits cannot keep governance current in a rapidly evolving AI environment.
NEW QUESTION # 21
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