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[General] AAISM Originale Fragen, AAISM Lerntipps

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【General】 AAISM Originale Fragen, AAISM Lerntipps

Posted at yesterday 21:16      View：2 | Replies：0        Print      Only Author   [Copy Link] 1#
Sie können im Internet teilweise die Fragen und Antworten zur ISACA AAISM Zertifizierungsprüfung von DeutschPrüfung kostenlos herunterladen, so dass Sie unsere Qualität testen können. Solange Sie unsere Produkte kaufen, versprechen wir Ihnen, dass wir alles tun würden, um Ihnen beim Bestehen der ISACA AAISM Prüfung zu helfen.
ISACA AAISM Prüfungsplan:
ThemaEinzelheiten
Thema 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.
Thema 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.
Thema 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 AAISM Prüfungsfragen mit Lösungen (Q89-Q94):89. Frage
Which of the following is the MOST effective way to prevent a model inversion attack?
  • A. Implement differential privacy during model training
  • B. Utilize data pseudonymization
  • C. Ensure data minimization
  • D. Monitor model output for anomalies
Antwort: A
Begründung:
AAISM identifies differential privacy as the primary mitigation technique against model inversion attacks, which attempt to reconstruct sensitive training data by probing model outputs.
Pseudonymization (B) and minimization (D) reduce exposure but do not prevent inversion. Output monitoring (A) detects anomalies but doesn't block reconstruction.
References: AAISM Study Guide - Privacy Attacks and Defenses; Differential Privacy.

90. Frage
What BEST protects trade secrets related to AI technologies during their life cycle?
  • A. Watermarking AI output
  • B. Restricting access to sensitive data
  • C. Patenting AI algorithms and data
  • D. Enforcing trademark rights
Antwort: B
Begründung:
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.

91. Frage
A large financial services organization is integrating a third-party AI solution into its critical fraud detection system. Which of the following is the BEST way for the organization to reduce risk associated with AI vendor and supply chain dependencies?
  • A. Conducting annual vulnerability assessments of the fraud detection system after integration
  • B. Focusing on performance testing to ensure the solution meets operational requirements
  • C. Implementing isolated virtual environments to validate the integration of the fraud detection system with the solution
  • D. Establishing contractual agreements requiring vendors to provide evidence of secure development practices
Antwort: D
Begründung:
AAISM emphasizes supplier assurance through contractual obligations as the foundational control for AI supply chain risk. Contracts should require verifiable evidence of secure development practices (e.g., secure SDLC, model and data provenance documentation, SBOM/MBOM where applicable, vulnerability disclosure, patch SLAs, audit rights, incident notification, and regulatory compliance assertions). This creates enforceable, continuous assurance beyond point-in-time tests.
* A is necessary but reactive and limited to your environment.
* B addresses performance, not supply chain security.
* D is a good isolation/validation practice but does not create vendor accountability across the lifecycle.
References:* AI Security Management™ (AAISM) Body of Knowledge: Third-Party and Supply Chain Governance-Contractual security requirements, evidence-based assurance, right-to-audit.* AI Security Management™ Study Guide: Vendor due diligence artifacts, secure development evidence, lifecycle obligations for AI providers.

92. Frage
Which of the following information is MOST important to include in a centralized AI inventory?
  • A. Training data sets
  • B. AI model use cases
  • C. Foundation model and package registry
  • D. Ownership and accountability of AI systems
Antwort: D
Begründung:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.
References:
AAISM Exam Content Outline - AI Governance and Program Management (AI Inventories and Oversight) AI Security Management Study Guide - Ownership and Accountability Structures

93. Frage
A health services organization is developing a proprietary generative AI chatbot to assist patients with medical devices. Which of the following should be the organization's HIGHEST priority?
  • A. Maximizing neural network size
  • B. Selecting the appropriate training data
  • C. Tuning algorithms used in the AI model
  • D. Maximizing the amount of training data
Antwort: B
Begründung:
AAISM prioritizes training data suitability-lawful sourcing, provenance, quality, representativeness, and safety-especially in health-related applications. The correctness and appropriateness of training data determine clinical safety, reduction of harmful outputs, and compliance with data protection/sector obligations. Larger models or more data do not compensate for inappropriate or low-quality datasets; tuning is secondary to ensuring the right data with rigorous curation, labeling quality, and guardrails aligned to patient safety requirements.
References:* AI Security Management (AAISM) Body of Knowledge: Data Governance & Quality; High- Risk/Health Context Controls; Safety & Harm Minimization* AAISM Study Guide: Data Provenance & Suitability, Domain-Specific Dataset Controls; Compliance-by-Design for Sensitive Sectors

94. Frage
......
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AAISM Lerntipps: https://www.deutschpruefung.com/AAISM-deutsch-pruefungsfragen.html
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