AAISM試験対策 & AAISM合格率

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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
AI Governance and Program Management	31%	- Frameworks and Compliance 1. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001) 2. Privacy and ethics considerations 3. Regulatory requirements - AI Strategy and Policy 1. Responsible and acceptable use 2. AI strategy development 3. Policies and procedures - AI Governance Foundations 1. Roles and responsibilities 2. Stakeholder considerations 3. Organizational structure and governance
AI Technologies and Controls	38%	- AI Assurance and Safety 1. Ethical AI controls 2. Model validation and testing 3. Bias mitigation - Data and Model Controls 1. Monitoring and runtime controls 2. Privacy-preserving techniques 3. Data protection and governance - AI Security Architecture 1. Model lifecycle security 2. Secure AI system design
AI Risk Management	31%	- AI Threats and Vulnerabilities 1. Adversarial AI and ML attacks 2. Supply chain and vendor risks 3. Data poisoning and model manipulation - AI Risk Assessment 1. Risk identification and analysis 2. Risk thresholds and treatment

Section

Weight

Objectives

AI Governance and Program Management

31%

- Frameworks and Compliance

1. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001)
2. Privacy and ethics considerations
3. Regulatory requirements

- AI Strategy and Policy

1. Responsible and acceptable use
2. AI strategy development
3. Policies and procedures

- AI Governance Foundations

1. Roles and responsibilities
2. Stakeholder considerations
3. Organizational structure and governance

AI Technologies and Controls

38%

- AI Assurance and Safety

1. Ethical AI controls
2. Model validation and testing
3. Bias mitigation

- Data and Model Controls

1. Monitoring and runtime controls
2. Privacy-preserving techniques
3. Data protection and governance

- AI Security Architecture

1. Model lifecycle security
2. Secure AI system design

AI Risk Management

31%

- AI Threats and Vulnerabilities

1. Adversarial AI and ML attacks
2. Supply chain and vendor risks
3. Data poisoning and model manipulation

- AI Risk Assessment

1. Risk identification and analysis
2. Risk thresholds and treatment

>> AAISM試験対策 <<

AAISM合格率、AAISM最新対策問題

競争力が激しい社会において、IT仕事をする人は皆、我々TopexamのAAISMを通して自らの幸せを筑く建筑士になれます。我が社のISACAのAAISM習題を勉強して、最も良い結果を得ることができます。我々のAAISM習題さえ利用すれば試験の成功まで近くなると考えられます。

ISACA Advanced in AI Security Management (AAISM) Exam 認定 AAISM 試験問題 (Q35-Q40):

質問 # 35
Which of the following AI data management techniques involves creating validation and test data?

A. Learning
B. Training
C. Splitting
D. Annotating

正解：C

解説：
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.

質問 # 36
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Ensure the certificate of destruction is received and archived in line with data retention policies
B. Ensure backups are tested and access controls are recorded and audited to ensure compliance
C. Update governance policies based on lessons learned and ensure a feedback loop exists
D. Perform a post-destruction risk assessment to verify that there is no residual exposure of data

正解：A

解説：
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.

質問 # 37
Which of the following BEST addresses risk associated with hallucinations in AI systems?

A. Recursive chunking
B. Automated output validation
C. Human oversight
D. Content enrichment

正解：C

解説：
AAISM prescribes human-in-the-loop (HITL) controls as the primary safeguard for high-impact generative AI use cases to mitigate hallucination risk. Human oversight ensures critical outputs are reviewed, corrected, and approved before use, with accountability, escalation, and documented decision trails. Automated validators and enrichment help reduce errors but are secondary; recursive chunking is a prompting tactic, not a governance control.

質問 # 38
An AI research team is developing a natural language processing model that relies on several open-source libraries. Which of the following is the team's BEST course of action to ensure the integrity of the software packages used?

A. Use the latest version of all libraries from public repositories
B. Scan the packages and libraries for malware prior to installation
C. Retrain the model regularly to handle package and library updates
D. Maintain a list of frequently used libraries to ensure consistent application in projects

正解：B

解説：
AAISM's technical control guidance emphasizes that when using open-source libraries, the best safeguard for integrity is to scan the packages for malware before installation. This ensures that compromised or malicious code does not enter the AI system environment. Maintaining lists aids consistency but not security. Always using the latest versions may introduce unverified vulnerabilities. Retraining models addresses functionality but not software integrity. Therefore, the strongest protective measure is pre-installation malware scanning of open-source packages.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Software Supply Chain Security) AI Security Management Study Guide - Open-Source Package Risk Mitigation

質問 # 39
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. Ensuring model developers have been trained in AI risk
B. Performing model testing and validation
C. Fine-tuning the foundational model
D. Training the foundational model on large data sets

正解：C

解説：
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

質問 # 40
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