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ISACA AAISM Exam Overview:
| Certification Vendor: | ISACA |
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| Exam Name: | Advanced in AI Security Management (AAISM) Exam |
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| Exam Number: | AAISM |
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| Available Languages: | English, Spanish |
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| Certificate Validity Period: | 3 years (continuing professional education required for renewal) |
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| Passing Score: | 450/800 |
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| Exam Format: | Multiple-choice questions |
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| Exam Price: | USD 459 (member) / USD 599 (non-member) |
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| Real Exam Qty: | 90 |
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| Related Certifications: | CISM CISSP |
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| Exam Duration: | 150 minutes |
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| Recommended Training: | ISACA Official Training & Review Materials |
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| Exam Registration: | ISACA AAISM Credentialing Page |
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| Sample Questions: | ISACA AAISM Sample Questions |
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| Exam Way: | Computer-based exam (online proctored or authorized test center) |
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| Pre Condition: | Recommended: Active CISM or CISSP certification and experience in IT/security management or advisory roles |
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| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism |
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>> AAISM最新試験 <<
ISACA AAISM練習問題集 & AAISMテスト模擬問題集
ほぼすべてのJPNTestお客様がAAISM試験に合格し、AAISM試験トレントの助けを借りて関連する認定資格を簡単に取得できます。あなたが例外になることは不可能だと強く信じています。 したがって、ISACAのAAISM試験問題を選択すると、実際には、近い将来に昇進する機会が増えることを意味します。さらに、関連分野でAAISM認定で才能を示したとき、当然、あなたは ISACA Advanced in AI Security Management (AAISM) Examキャリアライフに大きな影響を与える可能性のある多くの著名人と友達の輪を広げてください。
ISACA AAISM 認定試験の出題範囲:
| トピック | 出題範囲 |
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| トピック 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.
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| トピック 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.
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| トピック 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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ISACA Advanced in AI Security Management (AAISM) Exam 認定 AAISM 試験問題 (Q334-Q339):
質問 # 334
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?
- A. Recall
- B. Specificity
- C. Precision
- D. F1 score
正解:A
解説:
AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.
References:
AAISM Study Guide - AI Technologies and Controls (Evaluation Metrics and Model Performance) ISACA AI Security Management - Model Accuracy and Completeness Assessments
質問 # 335
Which of the following BEST enables an organization to minimize risk from shadow AI?
- A. Implementing a robust framework for AI performance evaluation
- B. Implementing detective controls and training employees on the use of AI
- C. Prohibiting the use of any type of AI in the organization
- D. Prohibiting the use of shadow AI and establishing penalties for noncompliance
正解:B
解説:
Detective controls provide visibility into unauthorized AI usage, while employee training promotes awareness and responsible behavior. Together, they address both detection and prevention, making them the most effective approach to minimizing risks associated with shadow AI.
質問 # 336
A school district contracts a third-party provider for AI-based curriculum recommendations. Which of the following is the BEST way to ensure the vendor uses AI responsibly?
- A. Requiring the vendor to provide the model card
- B. Verifying the vendor has updated terms of service
- C. Confirming the AI solution supports single sign-on (SSO)
- D. Ensuring the vendor offers 24/7 technical support
正解:A
解説:
AAISM emphasizes transparency artifacts from vendors to enable due diligence and assurance. A model card documents intended use, data sources, limitations, performance across subgroups, known risks, and evaluation procedures-information necessary to assess safety, fairness, and compliance for sensitive contexts like education. SSO and support are useful operational features; generic ToS updates are insufficient without model-specific disclosures.
References: AI Security Management™ (AAISM) Body of Knowledge - Third-Party & Supply Chain Governance; Transparency Artifacts (Model Cards, Datasheets). AAISM Study Guide - Vendor Due Diligence Requirements; Documentation for Risk, Fairness, and Intended Use.
質問 # 337
During the deployment of a generative AI platform, a risk assessment highlighted threats such as data leakage and prompt manipulation. Which of the following is the BEST way to ensure appropriate control selection?
- A. Apply AI-specific controls from external frameworks without customization and initiate monitoring to expedite compliance
- B. Postpone control selection until deployment and address risk through enhanced monitoring
- C. Map identified AI threats to enterprise control catalogs and integrate AI-specific safeguards where gaps exist
- D. Rely primarily on vendor-provided security features and seek third-party certifications
正解:C
解説:
AAISM requires that control selection be threat-led and context-specific, aligning AI threats to the organization's existing enterprise control catalogs (security, privacy, resilience) and augmenting them with AI-specific safeguards where coverage is insufficient. This ensures consistency with the risk appetite, removes duplication, and closes AI-unique gaps (e.g., prompt injection, data leakage from context windows, model misuse). Generic reliance on vendors or uncustomized external frameworks does not ensure fit-for-purpose coverage, and deferring control selection to post-deployment contradicts proactive risk treatment.
質問 # 338
When deriving statistical information from AI systems, which source of risk is MOST important to address?
- A. Incomplete outputs
- B. Systemic bias in data sets
- C. Lack of data normalization
- D. Presence of hallucinations
正解:B
解説:
AAISM emphasizes systemic or structural bias as a high-impact risk because biased data leads directly to discriminatory insights or decisions when used for analytics or reporting. This risk affects fairness, compliance, and organizational reputation.
質問 # 339
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