AAISM信息資訊 - AAISM最新試題

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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.

>> AAISM信息資訊 <<

AAISM最新試題 & AAISM題庫分享

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最新的 ISACA AAISM Certification AAISM 免費考試真題 (Q105-Q110):

問題 #105
Which of the following is the MOST critical key risk indicator (KRI) for an AI system?

A. The amount of data in the model
B. The accuracy rate of the model
C. The rate of drift in the model
D. The response time of the model

答案：C

解題說明：
AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.
References:
AAISM Study Guide - AI Risk Management (Monitoring and Drift Detection) ISACA AI Security Management - Key Risk Indicators for AI Systems

問題 #106
An AI fraud detection system in a bank uses transaction data that includes customer names and account numbers. Which of the following BEST helps the bank comply with privacy regulations and data classification policies?

A. Security training to avoid leakage of personal data
B. Masking or tokenizing customer-related data in use
C. Conducting awareness training for AI developers
D. Performing periodic audits of the AI model for compliance

答案：B

解題說明：
Masking or tokenizing customer names and account numbers directly protects sensitive and regulated data while allowing the AI system to continue processing transactions. This is the most effective control for supporting compliance with privacy regulations and data classification requirements.

問題 #107
An organization has requested a developer to apply AI algorithms to existing modules in order to improve customer service quality. At this stage, which of the following should be considered FIRST?

A. The developer may need to be held accountable for business inquiries raised by customers
B. IT management may need to revise the service agreement if AI behavior cannot be predefined
C. Project sponsors may need to agree on a phased approach in order to ensure safe release
D. The organization may need to explain the performance of the applied AI algorithm

答案：B

解題說明：
According to AAISM governance principles, when AI functionality is added to existing services, the first consideration is contractual and service-level accountability. If AI outputs cannot be predefined, the existing service agreements may no longer reflect performance responsibilities or liability. Revising or updating the agreement ensures governance alignment, accountability, and risk management for AI-driven behavior. Phased approaches and performance explanations are valuable but occur later in project management. Developer accountability for customer inquiries is not a primary governance step. The most immediate consideration is revising service agreements when AI introduces new uncertainties.

問題 #108
Which of the following stakeholders would be MOST helpful to include on an incident response team investigating AI-specific incidents involving unusual or incorrect decisions?

A. Model owner
B. Privacy expert
C. Security expert
D. Data owner

答案：A

解題說明：
The model owner has the deepest understanding of how the AI model was designed, trained, and behaves. This expertise is essential for investigating unusual or incorrect decisions, identifying root causes, and determining whether the issue stems from model logic, data, or configuration.

問題 #109
Which of the following controls BEST mitigates the risk of data poisoning?

A. Data set restoration
B. Data validation
C. Digital watermarking
D. Intrusion detection

答案：B

解題說明：
The AAISM technical controls framework emphasizes data validation as the primary safeguard against data poisoning attacks. Poisoning occurs when attackers insert malicious or corrupted data into training sets. Validation techniques verify the quality, authenticity, and consistency of input data before training, preventing compromised samples from corrupting the model.
Restoration helps after compromise, watermarking protects ownership, and intrusion detection monitors networks rather than data quality. The most effective preventive measure is data validation.

問題 #110
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