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[General] ISACA AAISM Deutsch Prüfungsfragen - AAISM Testing Engine

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【General】 ISACA AAISM Deutsch Prüfungsfragen - AAISM Testing Engine

Posted at yesterday 17:25      View：10 | Replies：0        Print      Only Author   [Copy Link] 1#
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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 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.
Thema 3
  • 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.

ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q105-Q110):105. Frage
An organization is commissioning a third-party AI system using sensitive data. Which metric is MOST important to consider?
  • A. Service availability
  • B. Model response time
  • C. Accessibility rating
  • D. Accuracy thresholds
Antwort: D
Begründung:
AAISM specifies that when AI systems process sensitive or high-impact data (e.g., financial, identity, health), accuracy thresholds become critical risk indicators. Inaccurate predictions can cause harm, regulatory non- compliance, discrimination, or financial loss.
Availability (D) is important but secondary to correctness in sensitive decision scenarios. Response time (B) and accessibility ratings (A) do not outweigh the need for accurate, reliable predictions.
References: AAISM Study Guide - AI Model Risk Metrics; Accuracy Requirements for High-Impact Systems.

106. Frage
Which of the following is the MOST likely cause of model drift?
  • A. Perfect knowledge
  • B. Data poisoning
  • C. Model stealing
  • D. Membership inference
Antwort: B
Begründung:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data- centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high- likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from. In contrast:
* Perfect knowledge is an attacker capability descriptor, not a drift cause.
* Membership inference targets privacy of the training set and does not inherently shift data distributions.
* Model stealing targets IP/confidentiality; it does not change the victim model's data distribution or decision boundary in situ.
References:* AI Security Management (AAISM) Body of Knowledge: Model Risk & Drift; Data Integrity Risks; Adversarial ML-Poisoning vs. Evasion* AAISM Study Guide: Production Monitoring & Drift Management; Risk Scenarios-Data Poisoning Impacts and Controls* AAISM Mapping to Standards:
Lifecycle Risk Treatment-Robustness to Data Contamination; Continuous Monitoring and Feedback

107. Frage
Which of the following is the MOST critical success factor for an AI implementation project?
  • A. Ensuring AI risk is captured in the risk register
  • B. Mapping data throughout the life cycle
  • C. Obtaining senior management buy-in
  • D. Developing and using model cards
Antwort: C
Begründung:
AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.
References: AI Security Management (AAISM) Body of Knowledge - AI Program Governance; Executive Sponsorship & Accountability; Strategy-to-Control Alignment for Successful AI Delivery.

108. Frage
Which of the following technologies can be used to manage deepfake risk?
  • A. Blockchain
  • B. Adaptive authentication
  • C. Multi-factor authentication (MFA)
  • D. Systematic data tagging
Antwort: A
Begründung:
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.
References:
AAISM Study Guide - AI Technologies and Controls (Emerging Controls for Content Authenticity) ISACA AI Governance Guidance - Blockchain for Data Integrity and Deepfake Mitigation

109. Frage
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:
  • A. provide a basis for identification of threats and vulnerabilities
  • B. deploy fast and cost-efficient AI systems for rapidly changing environments
  • C. align the system components of AI with the business goals of the organization
  • D. ensure the development of powerful, efficient, and scalable AI systems
Antwort: C
Begründung:
An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is strategic and governance alignment so that technical choices and controls consistently realize organizational objectives.
References:* AI Security Management™ (AAISM) Body of Knowledge: AI Program Architecture - alignment of capabilities, processes, and controls to business objectives* AI Security Management™ Study Guide: Architecture-driven governance, traceability from business goals to technical and control design

110. Frage
......
Um die Interessen zu schützen, bietet unsere Website die online Prüfungen zur ISACA AAISM Zertifizierungsprüfung von Zertpruefung, die von den erfahrungsreichen IT-Experten nach den Bedürfnissen bearbeitet werden. Sie werden Ihnen nicht nur helfen, die ISACA AAISM Prüfung zu bestehen und auch eine bessere Zukunft zu haben.
AAISM Testing Engine: https://www.zertpruefung.de/AAISM_exam.html
https://www.examcollectionpass.com
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