2026年Topexamの最新AAISM PDFダンプおよびAAISM試験エンジンの無料共有：https://drive.google.com/open?id=1F2nN4ue9MaycDzjGBTNFREX_fdT9My5s
もちろん、試験に関連する資料を探しているとき、他の様々な資料を見つけることができます。しかし、調査や自分自身の試用の後、TopexamのAAISM問題集が試験の準備ツールに最適であることはわかります。Topexamの資料は試験に準備する時間が十分ではない受験生のために特別に開発されるものです。それはあなたを試験に準備するときにより多くの時間を節約させます。しかも、TopexamのAAISM問題集はあなたが一回で試験に合格することを保証します。また、問題集は随時更新されていますから、試験の内容やシラバスが変更されたら、Topexamは最新ニュースを与えることができます。
| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Related Certifications: | ISACA Advanced in AI Security Management (AAISM) |
| Real Exam Qty: | 90 |
| Certificate Validity Period: | 3 years |
| Exam Format: | Scenario-based Questions, Multiple Choice |
| Exam Duration: | 150 minutes |
| Passing Score: | 450/800 |
| Available Languages: | Spanish, English |
| Exam Price: | USD $399 |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Online remote proctored exam or test center exam |
| Pre Condition: | Candidates must hold an active CISM or CISSP certification. |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline |
弊社のTopexamは専門的、高品質のISACAのAAISM問題集を提供するサイトです。ISACAのAAISM問題集は専業化のチームが改革とともに、開発される最新版のことです。ISACAのAAISM問題集には、詳細かつ理解しやい解説があります。このように、客様は我々のAAISM問題集を手に入れて勉強したら、試験に合格できるかのを心配することはありません。
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質問 # 68
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?
正解：C
解説：
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.
質問 # 69
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?
正解：C
解説：
AAISM requires formal model change governance: documented justification, risk assessment, validation
/verification (V&V), approvals, and post-deployment monitoring when altering features, thresholds, or signals. Disabling risk indicators to reduce false positives without rigorous validation and controlled rollout reflects a failure in model validation and change control, which AAISM treats as a core safeguard against unintended harms and regulatory breaches.
References: AI Security Management™ (AAISM) Body of Knowledge - Model Risk Governance; Change Management & Approvals; Validation/Verification Requirements. AAISM Study Guide - Control Gates for Feature/Threshold Changes; Post-Change Monitoring and Backout Criteria.
質問 # 70
Which of the following AI data management techniques involves creating validation and test data?
正解：D
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 # 71
A financial services firm received a regulatory fine after a vendor switched its chatbot's AI model without due diligence, resulting in unethical investment advice to the firm's clients. Which of the following controls should be implemented by the firm to BEST prevent recurrence of this scenario?
正解：D
解説：
AAISM requires formal change management for AI systems, including vendor-initiated changes:
pre-approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records.
質問 # 72
An attacker used model poisoning to insert a backdoor into an open-source AI model, causing a data breach. Which of the following is the FIRST action to prevent reoccurrence?
正解：C
解説：
Performing a root cause analysis is the first priority because it identifies how the poisoning attack occurred and where controls failed. Securing the supply chain and validating model outputs directly address the compromised open-source model and help prevent similar backdoor insertions in future deployments.
質問 # 73
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AAISM模擬トレーリング: https://www.topexam.jp/AAISM_shiken.html
2026年Topexamの最新AAISM PDFダンプおよびAAISM試験エンジンの無料共有：https://drive.google.com/open?id=1F2nN4ue9MaycDzjGBTNFREX_fdT9My5s