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ISACA AAISM 考試大綱：

主題	簡介
主題 1	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.
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 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.

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AAISM考試題庫 - AAISM套裝

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最新的 ISACA AAISM Certification AAISM 免費考試真題 (Q229-Q234):

問題 #229
An organization's CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?

A. Ensure the AI technologies are included in the asset inventory
B. Begin grouping similar AI products and solutions together
C. Identify vulnerabilities related to the technologies in use
D. Assess risk levels based on risk appetite and regulatory requirements

答案：A

解題說明：
AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance.
Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.

問題 #230
An organization recently introduced a generative AI chatbot that can interact with users and answer their queries. Which of the following would BEST mitigate hallucination risk identified by the risk team?

A. Training the foundational model on large data sets
B. Fine-tuning the foundational model
C. Performing model testing and validation
D. Ensuring model developers have been trained in AI risk

答案：B

解題說明：
AAISM highlights fine-tuning foundational models as one of the most effective strategies for reducing hallucination risk. By tailoring the model with domain-specific, curated, and verified datasets, organizations can reduce the frequency of irrelevant or fabricated outputs. Testing and validation help evaluate risks but do not directly minimize hallucinations. Training on larger datasets may improve generalization but does not guarantee accuracy. Developer training in AI risk supports governance but is not a technical control against hallucinations. The best mitigation is fine-tuning to align the chatbot with trusted, context-specific knowledge.
References:
AAISM Study Guide - AI Risk Management (Hallucination and Output Integrity Risks) ISACA AI Security Management - Fine-tuning Generative Models

問題 #231
Which of the following types of attacks would a malicious actor MOST likely use to target an AI model's training data and compromise the accuracy of the model's outputs?

A. Data exfiltration
B. Inference attack
C. Denial of service (DoS)
D. Data poisoning

答案：D

解題說明：
Data poisoning directly targets the training dataset by injecting malicious or manipulated data, which degrades model accuracy and causes incorrect or biased outputs once the model is trained.

問題 #232
An organization engages its end users to be involved in the AI life cycle to validate security controls. Which of the following MOST effectively supports decision-making regarding ethical and safety controls?

A. Updating the software development life cycle methodology
B. Defining and documenting technical controls
C. Engaging independent expert assistance
D. Providing context and understanding potential risk

答案：D

解題說明：
Providing context and understanding potential risk enables end users to make informed judgments about how AI systems may impact safety and ethics in real-world scenarios, directly supporting effective decision-making for appropriate controls.

問題 #233
Secure aggregation enhances federated learning security by:

A. Ensuring client contributions remain confidential even if the server is compromised
B. Processing client updates in isolation
C. Applying differential privacy to training data
D. Encrypting individual model updates so only the server can access them

答案：A

解題說明：
AAISM explains that secure aggregation ensures the server only sees aggregated model updates--not individual client contributions--so privacy is preserved even if the server is breached.

問題 #234
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