AAISM熱門考古題 -最新AAISM試題

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ISACA AAISM 考試大綱：

主題簡介
主題 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.
主題 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.
主題 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.

>> AAISM熱門考古題 <<

最新ISACA AAISM試題，AAISM題庫下載

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最新的 ISACA AAISM Certification AAISM 免費考試真題 (Q101-Q106):

問題 #101
An organization concerned about the ethical and responsible use of a newly developed AI product should consider implementing:

答案：A

解題說明：
The AAISM framework highlights that organizations adopting AI must ensure accountability structures are in place to govern ethical and responsible use. An accountability model assigns clear responsibility for decisions, outputs, and risks related to AI systems. While model cards provide transparency about a model's design and performance, they are primarily documentation tools. Vendor monitoring focuses on third-party oversight, not internal accountability. Security by design improves resilience but does not by itself address ethical use. The governance approach that most directly supports responsible and ethical AI deployment is an accountability model.
References:
AAISM Study Guide - AI Governance and Program Management (Ethical AI and Accountability) ISACA AI Security Management - Responsible AI Practices


問題 #102
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?

答案：C

解題說明：
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
- proof of lawful data disposal
- auditability
- regulatory compliance
- defensibility during inspections


問題 #103
Which of the following assessments MOST effectively addresses concerns related to ethical and trustworthy AI use?

答案：A

解題說明：
A fundamental rights impact assessment evaluates how AI systems may affect human rights, fairness, discrimination, autonomy, and ethical principles. This makes it the most effective assessment for addressing concerns related to ethical and trustworthy AI use.


問題 #104
An organization plans to use AI to analyze the shopping patterns of its customers to predict interests and send targeted, customized marketing emails. Which of the following should be done FIRST?

答案：B

解題說明：
The first action, before any processing of personal data for AI-driven profiling and targeted communications, is to establish a lawful basis for processing. Under AAISM-aligned privacy governance, explicit and informed consent is prioritized for new or sensitive uses such as interest profiling and targeted marketing.
Consent ensures purpose limitation, transparency, and user control prior to model ingestion and campaign activation. Training teams, updating terms of service, or verifying contact details are important, but they do not provide legal authority to process data; therefore, they follow after consent is obtained.
References: AI Security Management (AAISM) Body of Knowledge - Privacy Governance and Lawful Basis; Purpose Limitation and Transparency; Consent Management in AI-enabled Marketing. AAISM Study Guide - Data Protection Controls for AI Profiling; Consent Capture and Record-Keeping.


問題 #105
Which of the following controls would BEST help to prevent data poisoning in AI models?

答案：C

解題說明：
The most direct preventative control against data poisoning is robust data validation/ingestion gating:
provenance checks, schema and constraint validation, anomaly/outlier screening, label consistency tests, and whitelist/blacklist source controls before data reaches training pipelines. Larger datasets (A) don't inherently prevent poisoning; monitoring (C) is detective; updating a foundation model (D) does not address tainted inputs entering the pipeline.
References: AI Security Management™ (AAISM) Body of Knowledge - Adversarial ML Threats and Training-Time Attacks; Secure Data Ingestion and Validation Controls. AAISM Study Guide - Poisoning Prevention: Provenance, Validation, and Sanitization Gates.


問題 #106
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