AAISM퍼펙트최신버전공부자료 - AAISM최신업데이트덤프

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ISACA AAISM 시험요강:

주제	소개
주제 1	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
주제 2	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
주제 3	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.

>> AAISM퍼펙트 최신버전 공부자료 <<

AAISM최신 업데이트 덤프 & AAISM인기자격증 덤프문제

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최신 ISACA AAISM Certification AAISM 무료샘플문제 (Q296-Q301):

질문 # 296
An organization develops and implements an AI-based plug-in for users that summarizes their individual emails. Which of the following is the GREATEST risk associated with this application?

A. Inadequate controls over parameters
B. Lack of application vulnerability scanning
C. Data format incompatibility
D. Insufficient rate limiting for APIs

정답：A

설명：
According to AAISM risk management guidance, the greatest risk in AI applications handling personal communication data is inadequate parameter controls, which may allow unintended access, manipulation, or leakage of sensitive information. Plug-ins that interact with emails must enforce strict parameter validation and security restrictions to prevent unauthorized or manipulated inputs. While vulnerability scanning, format incompatibility, and API rate limiting are valid concerns, they are secondary. The primary risk is a lack of strong parameter controls that could expose sensitive content.

질문 # 297
An organization deploys a large language model (LLM) to automate legal document analysis.
Which of the following is MOST critical before model deployment?

A. Verifying the model's dataset remains static during its operational lifespan
B. Preventing unauthorized access to the model documentation by external entities
C. Implementing input filtering controls on uploaded contracts
D. Conducting testing of model outputs to validate expected behavior

정답：D

설명：
Before deployment, it is most critical to validate that the model produces accurate, reliable, and expected outputs for legal document analysis. Testing model behavior helps identify errors, inconsistencies, hallucinations, or unsafe outcomes that could create significant legal and operational risks.

질문 # 298
Which of the following factors is MOST important for preserving user confidence and trust in generative AI systems?

A. Transparent disclosure and informed consent
B. Data anonymization
C. Access controls and secure storage solutions
D. Bias minimization

정답：A

설명：
AAISM risk guidance underscores that transparent disclosure and informed consent are the most important factors in maintaining user trust in generative AI. Users must clearly understand how outputs are created, what data sources are used, and how risks such as bias or misinformation are managed. While bias minimization, access controls, and anonymization contribute to technical or ethical robustness, they are not sufficient to preserve user trust. Trust requires openness and consent, which align with governance expectations for transparency and accountability.

질문 # 299
An organization is adopting multiple AI solutions for customer support and management is concerned about potential privacy and confidentiality violations due to the implementation of the new solution. Which of the following is the BEST risk mitigation strategy in this situation?

A. Rely on best practice manuals from AI vendors to guide how employees use the systems.
B. Implement key risk indicators (KRIs) to monitor AI usage and update the risk register.
C. Create responsible AI use policies that address ethics, acceptable use, and transparency.
D. Use consumer-level AI guidelines with rules, terms, and recommendations for end users.

정답：C

설명：
Establishing responsible AI use policies defines clear expectations for handling data, ethical use, and transparency, directly addressing privacy and confidentiality risks across multiple AI solutions and guiding consistent behavior organization-wide.

질문 # 300
Which of the following approaches BEST helps to reduce model bias?

A. Utilizing a more complex model architecture
B. Increasing the number of labels per instance
C. Decreasing the frequency of model updates
D. Ensuring diversity in training data sources

정답：D

설명：
AAISM frames bias risk primarily as a data problem. The most impactful mitigation is to ensure diversity and representativeness of training data sources, thereby reducing sampling bias and improving fairness across subpopulations.

질문 # 301
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