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Practice Test AAISM Pdf - Online AAISM Training Materials

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Practice Test AAISM Pdf - Online AAISM Training Materials

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ISACA AAISM Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 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.
Topic 3
  • 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.

Online ISACA AAISM Training Materials - Valid AAISM Test NotesVCEEngine exam study material is essential for candidates who want to appear for the ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) certification exams and clear it to validate their skill set. This preparation material comes with Up To 1 year OF Free Updates And Free Demos. Place your order now and get real ISACA AAISM Exam Questions with these offers.
ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q190-Q195):NEW QUESTION # 190
Which of the following information is MOST important to include in a centralized AI inventory?
  • A. Foundation model and package registry
  • B. AI model use cases
  • C. Ownership and accountability of AI systems
  • D. Training data sets
Answer: C
Explanation:
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

NEW QUESTION # 191
A vendor switched its chatbot's AI model without due diligence, causing unethical investment advice. What control BEST prevents this scenario?
  • A. Shared responsibility model
  • B. Data minimization
  • C. Master services agreement
  • D. Change management
Answer: D
Explanation:
AAISM stresses AI-specific change management as essential for vendor-driven or system-driven updates.
Proper change control includes:
* impact assessments
* ethical review
* risk evaluation
* approval checkpoints
* rollback plans
An MSA (A) supports contracts but does not manage operational change. Shared responsibility (C) describes roles, not change control. Data minimization (D) reduces exposure but doesn't control model updates.
References: AAISM Study Guide - AI Change Management; Vendor Model Update Controls.

NEW QUESTION # 192
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models leveraging fraud detection systems?
  • A. Increasing model training speed for an efficient launch
  • B. Reducing computational resources required for the model training phase
  • C. Enhancing the accuracy of predictions to desired levels
  • D. Protecting individual data contributions while allowing statistical analysis
Answer: D
Explanation:
Differential privacy aims to protect the privacy of any single individual's data contribution while still enabling useful aggregate learning and statistical analysis. Noise mechanisms are calibrated so that results remain informative for modeling (e.g., fraud patterns) without revealing whether any particular person's data was included or enabling inference about them. Accuracy, speed, and compute efficiency can be secondary considerations, but the primary objective is privacy protection with utility preserved.
References: AI Security Management (AAISM) Body of Knowledge: Privacy-Preserving ML; Differential Privacy Objectives and Mechanisms. AAISM Study Guide: Individual Contribution Protection; Utility- Privacy Trade-offs and Calibration in Applied Models.

NEW QUESTION # 193
An organization is updating its vendor arrangements to facilitate the safe adoption of AI technologies. Which of the following would be the PRIMARY challenge in delivering this initiative?
  • A. Insufficient legal team experience with AI
  • B. Unwillingness of large AI companies to accept updated terms
  • C. Inability to sufficiently identify shadow AI within the organization
  • D. Failure to adequately assess AI risk
Answer: B
Explanation:
In the AAISM™ guidance, vendor management for AI adoption highlights that large AI providers often resist contractual changes, particularly when customers seek to impose stricter security, transparency, or ethical obligations. The official study materials emphasize that while organizations must evaluate AI risk and build internal expertise, the primary challenge lies in negotiating acceptable contractual terms with dominant AI vendors who may not be willing to adjust their standardized agreements. This resistance limits the ability of organizations to enforce oversight, bias controls, and compliance requirements contractually.
References:
AAISM Exam Content Outline - AI Risk Management
AI Security Management Study Guide - Third-Party and Vendor Risk

NEW QUESTION # 194
An aerospace manufacturer prioritizing accuracy and security wants to use generative AI. Which LLM adoption plan BEST aligns with its risk appetite?
  • A. Developing a private LLM to automate non-critical functions
  • B. Purchasing an LLM dataset on the open market
  • C. Developing a public LLM to automate critical functions
  • D. Contracting LLM access from a reputable third-party provider
Answer: A
Explanation:
AAISM notes that high-security industries (e.g., aerospace) should prefer private, controlled environments with restricted data exposure. Developing a private LLM for non-critical workloads minimizes operational and security risk while enabling innovation.
Public LLMs for critical functions (C) violate safety expectations. Purchased datasets (D) introduce unknown provenance. Outsourcing (B) increases third-party risk.
References: AAISM Study Guide - AI Governance; Safe LLM Adoption Strategies.

NEW QUESTION # 195
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