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[General] AAISM Exam Discount, AAISM Valid Braindumps Sheet

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【General】 AAISM Exam Discount, AAISM Valid Braindumps Sheet

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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 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.
Topic 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q149-Q154):NEW QUESTION # 149
Which of the following would BEST help to prevent the compromise of a facial recognition AI system through the use of alterations in facial appearance?
  • A. Fine-tuning the AI model to decrease hallucinations
  • B. Monitoring the system for misuse cases
  • C. Implementing a secondary AI system to confirm images
  • D. Enhancing training data to increase variance
Answer: D
Explanation:
AAISM materials note that adversaries may attempt to bypass facial recognition by disguising or altering appearance. The most effective mitigation is to enhance training data with a wide range of variances in facial features, lighting, and disguises so the system can robustly detect authentic users despite adversarial attempts.
Monitoring and secondary confirmation are supportive controls but are reactive. Fine-tuning to reduce hallucinations is irrelevant in this context, as hallucinations apply more to generative AI. The best preventive measure is strengthening the model with diverse, variance-rich training data.
References:
AAISM Study Guide - AI Technologies and Controls (Robust Training Data Strategies) ISACA AI Security Management - Biometric AI Security Risks

NEW QUESTION # 150
An automotive manufacturer uses AI-enabled sensors on machinery to monitor variables such as vibration, temperature, and pressure. Which of the following BEST demonstrates how this approach contributes to operational resilience?
  • A. Scheduling repairs for critical equipment based on real-time condition monitoring
  • B. Automating equipment repairs without any human intervention
  • C. Performing regular maintenance based on manufacturer recommendations
  • D. Conducting monthly manual reviews of maintenance schedules
Answer: A
Explanation:
AAISM highlights that AI-enabled predictive maintenance improves operational resilience by using real-time sensor monitoring to schedule repairs based on actual conditions rather than fixed schedules. This prevents unexpected breakdowns, reduces downtime, and ensures continuity of operations. Regular maintenance based on recommendations is static and may not reflect real conditions. Manual reviews are slow and inefficient.
Full automation of repairs without human oversight is not realistic or safe in critical manufacturing. The approach that best demonstrates resilience is real-time condition-based repair scheduling.
References:
AAISM Study Guide - AI Risk Management (Operational Resilience and Predictive Maintenance) ISACA AI Security Management - AI for Critical Infrastructure Reliability

NEW QUESTION # 151
Which AI data management technique involves creating validation and test data?
  • A. Annotating
  • B. Learning
  • C. Splitting
  • D. Training
Answer: C
Explanation:
AAISM describes data splitting as the process of dividing datasets into:
* training
* validation
* test sets
This is essential for reducing overfitting and ensuring robust evaluation.
Learning (A) refers to model training. Annotating (D) labels data. Training (C) does not create validation/test data.
References: AAISM Study Guide - AI Data Preparation & Dataset Splitting.

NEW QUESTION # 152
Which of the following is the PRIMARY purpose of a dedicated AI system policy?
  • A. Providing a framework to set AI objectives
  • B. Optimizing AI accuracy
  • C. Complying with external regulations
  • D. Ensuring environmental impact is minimized
Answer: A
Explanation:
Per AAISM, an AI policy is a governance instrument that defines objectives, principles, roles, responsibilities, accountability, and control requirements for AI systems across their lifecycle. It establishes the framework within which performance, compliance, ethics, risk appetite, security, privacy, and sustainability objectives are set and operationalized. Environmental considerations (A), accuracy optimization (B), and regulatory compliance (D) are important outcomes addressed under the policy, but the primary purpose is to provide the overarching framework for objectives and controls.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Governance Frameworks; Policies, Standards, and Procedures; Roles and Accountability in AI Programs.

NEW QUESTION # 153
Which of the following is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?
  • A. Bias and ethical practices
  • B. Security monitoring and alerting
  • C. Proposed regulatory enhancements
  • D. Access to the model
Answer: D
Explanation:
When enabling genAI capabilities in a critical system, AAISM prioritizes controlling access to the model and its interfaces (prompt surfaces, context windows, tools/functions, and connected data) because exposure expands the attack surface for prompt injection, data exfiltration, jailbreaks, and misuse. Monitoring (C) is necessary but detective; ethics and bias (D) are vital but secondary to immediate safety and security of a mission-critical environment; proposed regulations (B) are not an immediate operational risk.
References: AAISM Body of Knowledge: GenAI Security-Access Governance, Interface Hardening, and Prompt Surface Controls; AAISM Study Guide: Critical System Safeguards-Least Privilege, Guardrails, and Abuse Prevention.

NEW QUESTION # 154
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