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今の多くのIT者が参加している試験に、ISACAのAAISM認定試験「ISACA Advanced in AI Security Management (AAISM) Exam」がとても人気がある一つとして、合格するために豊富な知識と経験が必要です。ISACAのAAISM認定試験に準備する練習ツールや訓練機関に通学しなればまりませんでしょう。JPTestKingは君のもっともよい選択ですよ。多くIT者になりたい方にISACAのAAISM認定試験に関する問題集を準備しております。君に短い時間に大量のITの専門知識を補充させています。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Program Management | 31% | - AI asset and data lifecycle management
|
| Topic 2: AI Technologies and Controls | 38% | - Data management and privacy controls
|
| Topic 3: AI Risk Management | 31% | - AI risk assessment and treatment
|
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質問 # 96
When deriving statistical information generated by AI systems, which of the following types of risk is MOST important to address?
正解：A
解説：
The most critical risk when deriving statistical insights from AI-generated data is systemic bias in data.
According to the AI Security Management™ (AAISM) framework, systemic bias directly undermines the fairness, reliability, and validity of analytical results derived from AI systems. If the input data or learned model patterns are biased-reflecting skewed representation, sampling imbalance, or embedded prejudice- the statistical outputs will propagate and amplify these biases, leading to misinformed decisions and compliance violations.
Why Option A is Correct:
* Systemic bias affects the integrity and trustworthiness of AI-generated statistical information.
* It can introduce discriminatory outcomes, ethical breaches, and regulatory non-compliance-key concerns in AAISM's AI Risk Management and Governance principles.
* Mitigating systemic bias requires data quality assessments, fairness audits, bias detection tools, and model interpretability measures to ensure the derived insights are accurate and ethically sound.
Why Other Options Are Incorrect:
* Option B: Incomplete outputs can affect accuracy but are typically handled through process monitoring or retraining, not as a primary risk factor in statistical validity.
* Option C: Lack of data normalization is a technical preprocessing issue, not a governance-level risk impacting statistical trustworthiness.
* Option D: Hallucinations occur mainly in generative models (e.g., LLMs) and affect content generation, not statistical computation pipelines.
Exact Extract from Official AAISM Study Guide:
"Systemic bias in AI training and inference data represents the most material statistical risk. Bias propagates through derived metrics, predictive models, and decision outputs, compromising fairness, accuracy, and compliance. AI Security Management requires implementing bias detection, fairness testing, and governance mechanisms to identify and mitigate such systemic bias before using AI-generated analytics for organizational or regulatory reporting." References:
AI Security Management™ (AAISM) Body of Knowledge: AI Risk Identification and Evaluation, Bias and Fairness Management in AI Systems.
AI Security Management™ Study Guide: Systemic Bias Mitigation Techniques, Fairness Assurance in AI Analytics.
ISO/IEC 23894:2023 - Clause 7.2: Bias identification and treatment within AI risk frameworks.
質問 # 97
Implementing which of the following would MOST effectively address bias in generative AI models?
正解：A
解説：
AAISM identifies fairness constraints (e.g., constrained optimization, debiasing objectives, conditional generation controls, and post-processing calibrations) as the most direct, measurable method to mitigate disparate outcomes in generative systems. While data augmentation can help with coverage, and adversarial training improves robustness, fairness constraints explicitly target distributional fairness and outcome equity in generated content, aligning with governance and compliance goals.
References: AI Security Management™ (AAISM) Body of Knowledge - Fairness & Bias Management in Generative AI; Metrics, Constraints, and Remediation. AAISM Study Guide - Fairness Objectives, Post-hoc Debiasing, and Evaluation Protocols.
質問 # 98
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?
正解：D
解説：
Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack-data pipelines, feature stores, model registries, inference services, and dependent infrastructure-can actually fail over and recover within RTO
/RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents.
References:AI Security Management™ (AAISM) Body of Knowledge: Business Continuity & Disaster Recovery for AI; Validation and Exercising of Continuity Plans; RTO/RPO for Models, Data, and Pipelines.
AAISM Study Guide: Operational Resilience for AI Systems; BCP/DR Test Scenarios (model registry, feature store, pipeline recovery); Continuity Metrics and Evidence of Readiness.
質問 # 99
An organization is implementing an AI-based credit assessment engine using internal and third-party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?
正解：C
解説：
The strongest alignment with AI life-cycle data management controls is establishing documented procedures for data sourcing, lineage tracking, and quality validation. Governance requires end-to-end data controls- provenance, consent, sourcing approvals, lineage, quality checks, labeling standards, retention, and auditability-so models are trained and validated on trustworthy, compliant data. Hashing alone is pseudo- anonymization and does not deliver full governance. Restricting to structured data is an implementation constraint, not a control set. Encryption and access control protect confidentiality/availability but do not ensure lineage or quality.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance & Stewardship; AI Data Lifecycle Controls; Data Provenance, Lineage, and Quality Assurance. AAISM Study Guide: Data Intake Procedures; Lineage and Audit Evidence; Quality Gates and Approval Workflows.
質問 # 100
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?
正解：B
解説：
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
Post-destruction risk assessments (A) are not primary compliance evidence. Backup tests (B) are operational tasks, not decommissioning proof. Policy updates (C) are future improvements.
References: AAISM Study Guide - AI Decommissioning & Regulatory Compliance; Data Destruction Evidence.
質問 # 101
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AAISM学習ガイドでは、いつでもどこでも学習できます。学習時間を保証できない場合は、AAISM学習ガイドが最適です。随時学習し、学習に利用できるすべての時間を最大限に活用できるためです。オンライン版のAAISMラーニングガイドでは、デバイスの使用を制限していません。コンピューターを使用することも、携帯電話を使用することもできます。いつでも便利だと思うデバイスを選択できます。さらに、AAISM試験に問題なく合格できます。
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