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[General] AAISM Exam Quiz | AAISM Reliable Test Sims

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【General】 AAISM Exam Quiz | AAISM Reliable Test Sims

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The web-based ISACA AAISM practice exam is compatible with all browsers like Chrome, Mozilla Firefox, MS Edge, Internet Explorer, Safari, Opera, and more. Unlike the desktop version, it requires an internet connection. The ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) practice exam will ask real ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) exam questions.
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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q204-Q209):NEW QUESTION # 204
Security and assurance requirements for AI systems should FIRST be embedded in the:
  • A. Model design phase
  • B. Model testing phase
  • C. Model deployment phase
  • D. Model training phase
Answer: A
Explanation:
AAISM directs organizations to embed security, safety, and compliance controls at design time ("secure- by-design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non-functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.
References:* AI Security Management (AAISM) Body of Knowledge: Governance-Secure-by-Design; Policy-to-Control Traceability; Requirements Management* AAISM Study Guide: AI Program Lifecycle- Planning & Design Controls; Design-time Threat Modeling and Control Selection* AAISM Mapping to Standards: Design-phase Risk Identification and Requirements Engineering for AI

NEW QUESTION # 205
Which of the following AI data management techniques involves creating validation and test data?
  • A. Learning
  • B. Splitting
  • C. Training
  • D. Annotating
Answer: B
Explanation:
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.

NEW QUESTION # 206
When creating a use case for an AI model that provides sensitive decisions affecting end users, which of the following is the GREATEST benefit of using model cards?
  • A. Data collection requirements are reduced
  • B. Ethical considerations of the model are documented
  • C. Technical instructions for model deployment are created
  • D. Model type selection is documented
Answer: B
Explanation:
AAISM highlights that model cards are a governance tool designed to document ethical considerations, limitations, fairness constraints, data sources, and suitability of use cases for AI models-especially when they affect individuals' rights, opportunities, or access to services.
Their greatest value is providing transparency and ethical clarity, ensuring stakeholders understand risks, bias considerations, and how decisions impact users.
Deployment instructions (B) are not part of model cards. They do not reduce data needs (C), nor is model type selection (D) their primary purpose.
References: AAISM Study Guide - AI Governance Documentation; Transparency and Model Cards.

NEW QUESTION # 207
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models leveraging fraud detection systems?
  • A. Reducing computational resources required for the model training phase
  • B. Protecting individual data contributions while allowing statistical analysis
  • C. Increasing model training speed for an efficient launch
  • D. Enhancing the accuracy of predictions to desired levels
Answer: B
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 # 208
An organization is implementing AI agent development across engineering teams. What should AI-specific training focus on?
  • A. Prompt injection, agent memory control, insecure tool execution
  • B. API abuse, data leakage, third-party plug-in risk
  • C. Output moderation, hallucination handling, policy alignment
  • D. Dataset bias, explainability, fairness
Answer: A
Explanation:
AAISM states that AI agent security training should focus on the unique risks of agentic systems, which include:
* prompt injection
* memory control and context hijacking
* unsafe tool execution (agents triggering unauthorized actions)
These risks are specific to autonomous or semi-autonomous AI agents.
Bias, fairness (B) and output moderation (C) are important but not the most critical for agent security. API abuse and plug-in risk (D) matter but are secondary.
References: AAISM Study Guide - Agentic AI Security; Prompt Injection and Tool Execution Risks.

NEW QUESTION # 209
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