[2026年03月] 問題集簡単概要AAISM試験問題Fast2test
AAISMトレーニング認証最新版をゲットIsaca Certification
質問 # 28
To ensure the ethical and responsible use of AI, which of the following AI usage policy metrics is MOST important for an organization to monitor?
- A. Frequency of policy consultations by employees
- B. Number of reported policy violations
- C. Frequency of policy reviews and updates
- D. Number of AI projects that have undergone policy compliance review
正解：D
解説：
AAISM emphasizes governance effectiveness metrics tied to real lifecycle checkpoints. The count (and percentage) of AI projects that completed policy compliance review before deployment is a leading indicator of policy enforcement and assurance. It directly reflects whether responsible-AI requirements (risk assessment, impact assessment, data/privacy checks, security controls) are embedded in practice. Consult frequency (A) and review cadence (D) are activity metrics, not outcomes. Reported violations (B) are lagging indicators and can be deceptive (low numbers may indicate under-reporting).
References:* AI Security Management™ (AAISM) Body of Knowledge: Program KPIs-policy adoption, stage-gate compliance, audit readiness* AAISM Study Guide: Governance metrics for Responsible AI- coverage of reviews, pass/fail rates, exceptions handling
質問 # 29
Which of the following types of data is used to tune hyperparameters?
- A. Validation
- B. Configuration
- C. Test
- D. Training
正解：A
解説：
According to AAISM, hyperparameter tuning uses validation data, not training or test data. Validation datasets are specifically designed to evaluate different hyperparameter configurations without contaminating the training or testing sets.
Training data (C) teaches the model. Test data (D) is used only for final evaluation. Configuration data (B) is unrelated to performance optimization.
References: AAISM Study Guide - AI Model Evaluation; Role of Validation Data in Hyperparameter Tuning.
質問 # 30
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?
- A. Data preparation
- B. Data collection
- C. Data normalization
- D. Data minimization
正解：A
解説：
According to AAISM's data life-cycle model, data preparation is the phase where raw data is transformed into a model-ready format. The materials describe this phase as including "cleaning, encoding, formatting, feature engineering, and other transformations required for model consumption." This directly matches the scenario where a team formats user information to be readable by an AI model. Data minimization (A) is about reducing data to the minimum necessary for the stated purpose. Data collection (C) focuses on acquiring data from different sources. Data normalization (D) is a specific technique (often a sub-activity within preparation) that adjusts numeric values to a common scale; it is narrower than the broader concept of preparation.
Therefore, the activity described is correctly associated with data preparation, which the AAISM framework clearly positions before training and evaluation.
References: AI Security Management™ (AAISM) Study Guide - AI Data Life Cycle; Data Preparation and Pre-processing.
質問 # 31
Which area of intellectual property law presents the GREATEST challenge in determining copyright protection for AI-generated content?
- A. Determining the rightful ownership of AI-generated creations
- B. Enforcing trademark rights associated with AI systems
- C. Protecting trade secrets in AI technologies
- D. Establishing licensing frameworks for AI-generated works
正解：A
解説：
AAISM governance content highlights that the greatest intellectual property challenge in the context of AI- generated works is determining rightful ownership. Traditional copyright law requires human authorship, but AI-generated creations blur authorship and ownership boundaries, raising legal uncertainty about who can claim rights. Trademark enforcement, trade secret protection, and licensing frameworks are established areas of IP law but do not present the same fundamental challenge as ownership attribution. For AI-generated content, the central legal dilemma is ownership of the creation.
References:
AAISM Study Guide - AI Governance and Program Management (Intellectual Property and AI) ISACA AI Security Management - Copyright and Ownership Challenges
質問 # 32
To ensure ethical and responsible AI use, which AI usage policy metric is MOST important to monitor?
- A. Number of policy violations
- B. Frequency of policy reviews and updates
- C. Frequency of policy consultations by employees
- D. Number of AI projects reviewed for compliance
正解：C
解説：
AAISM states the most meaningful policy performance metric is how often employees consult AI policies, which reflects:
* awareness
* practical adoption
* reliance on policy guidance
* safe decision-making behavior
Violations (A) are lagging indicators. Compliance reviews (B) measure oversight, not behavior. Policy review frequency (D) tracks governance updates, not usage.
References: AAISM Study Guide - AI Policy Effectiveness Metrics.
質問 # 33
Which AI model is BEST suited to ensure explainability in an HR department's pre-screening tool for candidate resumes?
- A. Gradient boosting machine
- B. Neural network
- C. Decision tree
- D. Support vector machine
正解：C
解説：
According to AAISM, decision trees provide the highest explainability because their structure clearly shows how inputs map to decisions. This is essential in HR applications subject to fairness, bias, and compliance requirements.
SVMs (A) and gradient boosting (D) are less interpretable. Neural networks (B) are explicitly listed as low- explainability models.
References: AAISM Study Guide - Explainability and Transparency Requirements; Interpretable ML Models.
質問 # 34
A financial organization is concerned about the risk of prompt injection attacks on its customer service chatbot. Which of the following controls BEST addresses this concern?
- A. Continuous monitoring
- B. Human-in-the-loop
- C. Input validation
- D. Increasing model parameters
正解：C
解説：
AAISM describes prompt injection as an attack where adversaries craft inputs that manipulate model behavior or override system instructions. The recommended control pattern is to implement robust input validation and constraint mechanisms that sanitize and structure user inputs before they are processed by the model. The guidance includes techniques such as template-based prompts, restricted instruction sets, and validation rules to filter malicious or out-of-scope content. Human-in-the-loop (A) provides oversight but may not scale and is not a primary technical protection. Increasing model parameters (C) relates to capacity and performance, not security. Continuous monitoring (D) is important for detection but does not prevent prompt injection at the point of entry. Therefore, input validation, combined with controlled prompt construction, is identified as the best direct control against prompt injection attacks in customer-facing chatbots.
References: AI Security Management™ (AAISM) Study Guide - AI Threats and Attack Vectors; Prompt Injection and Input Control Mechanisms.
質問 # 35
Which of the following reviews MUST be conducted as part of an AI impact assessment?
- A. Testing, evaluation, validation, and verification
- B. Identification of environmental and societal consequences
- C. Security control self-assessment (CSA)
- D. Evaluation of model reproducibility
正解：B
解説：
An AI impact assessment is a governance instrument that must address potential impacts on people and society, including environmental and societal consequences. This review determines downstream effects (e.g., fairness, safety, rights, sustainability) before and during deployment, supporting accountability and compliance. While TEVV activities (testing, evaluation, validation, verification) and security control self- assessments are integral to assurance and security management, the defining obligation of an impact assessment is to evaluate potential societal and environmental outcomes and associated mitigations. Model reproducibility is a technical quality attribute but is not the mandatory core of an impact assessment.
References:* AI Security Management (AAISM) Body of Knowledge: Impact Assessment - governance requirements for societal, environmental, and stakeholder impact review* AI Security Management Study Guide: AI impact assessment scope, stakeholder impact analysis, and documentation of societal and environmental consequences
質問 # 36
Which of the following BEST describes an adversarial attack on an AI model?
- A. Reverse engineering the AI model using social engineering techniques
- B. Conducting denial-of-service (DoS) attacks against AI APIs
- C. Attacking the underlying hardware of the AI system
- D. Providing inputs that mislead the AI model into incorrect predictions
正解：D
解説：
In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se. Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.
References:AI Security Management (AAISM) Body of Knowledge: Taxonomy of AI Threats- Adversarial Examples and Evasion; Distinction from System/Network Attacks.AAISM Study Guide:
Definitions and Threat Models in Adversarial ML; Attack-Defense Mapping for Model Predictions.
質問 # 37
Which of the following methods provides the MOST effective protection against model inversion attacks?
- A. Using adversarial training
- B. Implementing regularization output
- C. Increasing the number of training iterations
- D. Reducing the model's complexity
正解：B
解説：
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management™ Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.
質問 # 38
When evaluating a new AI tool for intrusion prevention, which of the following is the MOST important consideration to ensure the tool fits within the existing program architecture?
- A. Ensure automated response orchestration.
- B. Confirm tool capabilities align with the control objectives.
- C. Prioritize a tool that offers real-time anomaly detection.
- D. Select a tool that integrates with the existing SIEM.
正解：B
解説：
The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.
References:* AI Security Management™ (AAISM) Body of Knowledge: AI Governance and Program Management - Security program alignment, control objectives, and requirements traceability* AI Security Management™ Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance
質問 # 39
Which of the following is the MOST important consideration when deciding how to compose an AI red team?
- A. Resource availability
- B. AI use cases
- C. Compliance requirements
- D. Time-to-market constraints
正解：B
解説：
AAISM materials specify that the composition of an AI red team must be tailored to the organization's AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.
References:
AAISM Exam Content Outline - AI Risk Management (Red Teaming Considerations) AI Security Management Study Guide - Tailoring Adversarial Testing to Use Cases
質問 # 40
Which of the following BEST describes an adversarial attack on an AI model?
- A. Reverse engineering the AI model using social engineering techniques
- B. Conducting denial-of-service (DoS) attacks against AI APIs
- C. Attacking the underlying hardware of the AI system
- D. Providing inputs that mislead the AI model into incorrect predictions
正解：D
解説：
In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se.
Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.
References:AI Security Management™ (AAISM) Body of Knowledge: Taxonomy of AI Threats- Adversarial Examples and Evasion; Distinction from System/Network Attacks.AAISM Study Guide:
Definitions and Threat Models in Adversarial ML; Attack-Defense Mapping for Model Predictions.
質問 # 41
Which of the following BEST ensures AI components are validated as part of disaster recovery testing?
- A. Simulating denial of service (DoS) attacks against AI APIs to evaluate detection capabilities
- B. Running simulated data loss scenarios by erasing test records from the AI system's feature store
- C. Monitoring model performance metrics during failover and recovery to assess system stability
- D. Disconnecting primary model training clusters to test retraining workflow during extended outages
正解：C
解説：
Business continuity and disaster recovery (BC/DR) exercises for AI must validate that critical AI components (feature stores, model registries, inference services, pipelines) operate within agreed recovery objectives during failover and restoration. Monitoring and evaluating model performance and stability during DR tests provides objective evidence that AI services remain functional, accurate, and reliable under contingency conditions, thereby validating the AI stack end-to-end.
Option A focuses on retraining during outages (a niche scenario) rather than validating service continuity for production inference. Option B is security testing, not BC/DR validation. Option C tests data loss handling but does not comprehensively validate AI service behavior across failover and recovery.
References: AI Security Management (AAISM) Body of Knowledge: "Operational Resilience-BC/DR for AI Systems," "Validation and Evidence of Continuity"; AAISM Study Guide: "AI DR Test Planning- Metrics, Model Performance Validation, and Recovery Readiness."
質問 # 42
Which of the following AI data management techniques involves creating validation and test data?
- A. Annotating
- B. Learning
- C. Splitting
- D. Training
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.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.
質問 # 43
Which of the following would BEST help an organization align its AI initiatives with business objectives?
- A. Protecting enterprise information used by AI projects
- B. Complying with applicable AI-related regulations
- C. Ensuring ethical use of AI technologies in projects
- D. Establishing an AI governance committee
正解：D
解説：
An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.
References: AI Security Management (AAISM) Body of Knowledge: AI Governance Operating Model- Structures, Roles, and Decision Rights; AAISM Study Guide: Strategic Alignment, Portfolio Oversight, and Accountability Mechanisms.
質問 # 44
Which of the following BEST describes how supervised learning models help reduce false positives in cybersecurity threat detection?
- A. They use real-time feature engineering to automatically adjust decision boundaries
- B. They analyze patterns in data to group legitimate activity from actual threats
- C. They dynamically generate new labeled data sets
- D. They learn from historical labeled data
正解：D
解説：
According to AAISM technical content, supervised learning models reduce false positives by learning from historical labeled data that distinguishes between legitimate activity and actual threats. This training enables the model to recognize patterns and improve its discrimination ability over time. Grouping patterns (A) describes clustering, an unsupervised method. Real-time feature engineering (B) and generating new labeled data (D) are advanced techniques but not the fundamental supervised learning approach. The essence of supervised learning is leveraging labeled data to minimize misclassification, including false positives.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Machine Learning Approaches) AI Security Management Study Guide - Supervised Learning for Threat Detection
質問 # 45
What BEST protects trade secrets related to AI technologies during their life cycle?
- A. Patenting AI algorithms and data
- B. Restricting access to sensitive data
- C. Watermarking AI output
- D. Enforcing trademark rights
正解：B
解説：
AAISM emphasizes access control and data security as the strongest mechanisms to protect trade secrets, including:
* proprietary algorithms
* training datasets
* model weights
* internal design documentation
Trademarks (A) protect brand, not trade secrets. Patents (C) require public disclosure. Watermarks (D) protect generated content, not internal trade secrets.
References: AAISM Study Guide - AI Intellectual Property Protection & Access Controls.
質問 # 46
An organization has implemented a natural language processing model to respond to customer questions when personnel are not available. A pre-implementation security assessment revealed attackers could access sensitive company data through a chat interface injection attack. Which of the following is the BEST way to prevent this attack?
- A. Manually reviewing AI model outputs
- B. Implementing input validation and templates
- C. Conducting regular information security audits
- D. Ensuring continuous monitoring and data tagging
正解：B
解説：
To prevent prompt/interface injection, AAISM prioritizes preventive technical controls at the boundary:
input validation/sanitization, structured templates/system prompts, allow/deny lists, and context isolation. These measures constrain user-supplied content and block adversarial instructions from being interpreted as system directives. Monitoring (A) and audits (D) are detective/assurance activities; manual output review (B) is compensating but less scalable and does not prevent injection.
References: AI Security Management (AAISM) Body of Knowledge - Secure Prompting & Input Controls; Interface Injection Mitigations; Context and Instruction Isolation Patterns.
質問 # 47
Which of the following key risk indicators (KRIs) is MOST relevant when evaluating the effectiveness of an organization's AI risk management program?
- A. Percentage of critical business systems with AI components
- B. Number of AI-related training requests submitted
- C. Percentage of AI projects in compliance
- D. Number of AI models deployed into production
正解：C
解説：
AAISM identifies percentage of AI projects in compliance as the most relevant KRI for evaluating AI risk management effectiveness. This metric directly reflects adherence to governance, regulatory, and security requirements. The number of models deployed (A) or systems with AI components (B) indicate scale, not risk management quality. Training requests (D) show awareness levels but do not measure effectiveness of risk management. Compliance percentage provides a direct, measurable indication of how well risks are being governed and mitigated.
References:
AAISM Exam Content Outline - AI Risk Management (Risk Metrics and Compliance) AI Security Management Study Guide - Key Risk Indicators in AI Programs
質問 # 48
Which of the following is the BEST way to ensure role clarity and staff effectiveness when implementing AI- assisted security monitoring tools?
- A. Defer implementation until the security team can be expanded with data scientists.
- B. Increase training budgets for business staff to obtain vendor-neutral AI certifications.
- C. Update the security program to include cross-functional AI-specific responsibilities.
- D. Transition responsibilities for AI tools to external consultants for improved scalability.
正解：C
解説：
AAISM directs that AI adoption in security operations be governed through explicit operating models and RACI-mapped responsibilities spanning security operations, data science/ML, platform engineering, privacy, and compliance. Role clarity comes from updating the security program to codify AI-specific responsibilities (model monitoring, incident handling for AI failures, data governance, change control for models, bias/fairness reviews, supplier assurance) rather than deferring implementation, outsourcing core accountability, or relying on generic certifications. This ensures measurable accountability, reduces hand-off risk, and aligns day-to-day SOC practices with AI control objectives.
References:* AI Security Management (AAISM) Body of Knowledge: AI Governance Operating Model; Roles & Responsibilities; RACI for AI-enabled Security Operations* AAISM Study Guide: Program Governance, Control Objective Mapping to SOC Workflows; Cross-Functional Ownership for AI Controls
質問 # 49
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