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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment |
| AI Life Cycle Risk Management | - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks - AI model and data risk identification | |
| 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 |
今の競争の激しいIT業界ではISACAのAAIR試験にパスした方はメリットがおおくなります。給料もほかの人と比べて高くて仕事の内容も豊富です。でも、この試験はそれほど簡単ではありません。
質問 # 17
Which of the following is the GREATEST risk when an AI system requires a specific safeguard that cannot be put in place because of technical constraints?
正解：B
解説：
When required safeguards cannot be technically implemented, the risk they were designed to mitigate remains unaddressed. This creates a residual exposure gap where the AI system operates with known, unmitigated vulnerabilities-a fundamental risk management failure for the identified threat.
Why A is Correct: The ISACA AAIR risk treatment guidance identifies elevated residual exposure from absent controls as the greatest risk when required safeguards cannot be implemented. Every required safeguard addresses a specific risk exposure. When that safeguard is technically infeasible, the risk it was designed to prevent remains fully present. This unmitigated exposure may exceed the organization's risk tolerance and require escalation to senior management for risk acceptance or alternative treatment decisions.
Why B is Wrong: Training dataset restrictions relate to model development constraints, not directly to the inability to implement a specific runtime safeguard. This is a separate concern that may arise in some technical constraint scenarios but is not the primary risk of an absent safeguard.
Why C is Wrong: User experience degradation is an operational quality concern. Performance impacts from technical constraints are a usability issue rather than a risk exposure representing the greatest organizational concern.
Why D is Wrong: Operational inefficiency and manual process dependencies are resource and process concerns. While relevant to operational cost and effectiveness, they do not represent the primary risk of an unmitigated security or safety exposure from an absent safeguard.
質問 # 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?
正解：A
解説：
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.
質問 # 19
An organization is selecting an AI model for a solution that requires the creation of new content. It is MOST important to consider selecting:
正解：C
解説：
Different AI model architectures are optimized for different tasks. Content creation requires a model that can generate novel outputs-text, images, audio, or code-rather than classify, cluster, or optimize decisions based on rules or rewards.
Why A is Correct: According to ISACA AAIR AI technology selection guidance, generative models are specifically designed to synthesize new content by learning the underlying probability distributions of training data. They can produce novel, contextually appropriate outputs-exactly what content creation requires.
Large language models (LLMs), diffusion models, and GANs are generative architectures designed for this purpose.
Why B is Wrong: Unsupervised clustering groups existing data points by similarity but does not generate new content. It is used for pattern discovery and segmentation, not creative output generation.
Why C is Wrong: Rule-based expert systems execute predefined logic trees and cannot produce novel content beyond the rules explicitly encoded. They are rigid, deterministic systems unsuitable for open-ended content creation.
Why D is Wrong: Reinforcement learning optimizes decision sequences to maximize cumulative rewards. It is suited for sequential decision-making tasks (games, robotics, recommendation systems) but is not the appropriate architecture for direct content generation.
質問 # 20
An organization is integrating AI systems into core business operations and has decided to establish a formal process to align AI initiatives with corporate values. Which of the following is the GREATEST benefit of this decision?
正解：B
質問 # 21
A financial organization is developing an AI model for credit risk assessment. Which of the following is MOST important to ensure the training data supports accurate and unbiased outcomes?
正解：C
解説：
Credit risk assessment AI models trained on unrepresentative datasets perpetuate and amplify historical financial inequities, producing discriminatory outcomes that violate anti-discrimination laws and harm underrepresented borrowers. Dataset diversity is the primary safeguard against training-data-driven bias.
Why A is Correct: According to ISACA AAIR bias and fairness guidance for financial AI, dataset diversity is the most important factor for supporting accurate and unbiased credit risk outcomes. A diverse dataset that represents the full population of potential borrowers-across demographics, income levels, credit histories, and geographies-enables the model to learn genuine risk relationships rather than proxies for protected characteristics. Without diversity, even technically sophisticated models perpetuate discriminatory patterns from historical data.
Why B is Wrong: Supervised learning is a modeling approach, not a data quality characteristic. The choice of supervised learning is appropriate for credit scoring but does not determine whether the training data is representative or unbiased.
Why C is Wrong: Synthetic data augmentation can supplement real data to address specific gaps but cannot substitute for diversity in the underlying real-world data. Synthetic data derived from biased real data may amplify rather than correct the original bias.
Why D is Wrong: Data normalization is a preprocessing technique that scales numerical features to comparable ranges to improve model convergence. It addresses technical modeling quality but has no effect on the representational diversity or demographic fairness of the dataset.
質問 # 22
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