AAISM Hot Spot Questions & AAISM Exam Dump

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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.

>> AAISM Hot Spot Questions <<

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

Answer: D

Explanation:
A confusion matrix is explicitly defined in AAISM as the framework used to interpret classification performance by listing:
- true positives
- true negatives
- false positives
- false negatives


NEW QUESTION # 143
Which BEST addresses hallucination risk in AI systems?

Answer: D

Explanation:
AAISM states that human oversight is the strongest control for hallucination risks, especially in high-impact decisions. Humans validate outputs, correct errors, and override unsafe model responses.
Automated validation (C) helps but cannot fully detect hallucinations. Chunking (B) improves information handling but not hallucination mitigation. Content enrichment (D) does not reduce hallucinations.
References: AAISM Study Guide - Human-in-the-Loop (HITL) Controls; Hallucination Mitigation.


NEW QUESTION # 144
An organization plans to implement a new AI system. Which of the following is the MOST important factor in determining the level of risk monitoring activities required?

Answer: C

Explanation:
AAISM risk management guidance clarifies that the organization's risk tolerance is the most important factor in determining how much monitoring is needed. Risk tolerance specifies the amount of risk the organization is willing to accept and defines the threshold for triggering monitoring or mitigation activities. Risk appetite is broader and strategic, while tolerance sets the operational limits. The number of users may influence scale, and compensating controls may affect resilience, but neither dictates monitoring intensity as directly as risk tolerance.


NEW QUESTION # 145
To ensure the ethical and responsible use of AI, which of the following AI usage policy metrics is MOST important for an organization to monitor?

Answer: D

Explanation:
AAISM emphasizes governance effectiveness metrics tied to real lifecycle checkpoints. The count (and percentage) of AI projects that completed policy compliance review before deployment is a leading indicator of policy enforcement and assurance. It directly reflects whether responsible-AI requirements (risk assessment, impact assessment, data/privacy checks, security controls) are embedded in practice. Consult frequency (A) and review cadence (D) are activity metrics, not outcomes. Reported violations (B) are lagging indicators and can be deceptive (low numbers may indicate under-reporting).
References:* AI Security Management (AAISM) Body of Knowledge: Program KPIs-policy adoption, stage-gate compliance, audit readiness* AAISM Study Guide: Governance metrics for Responsible AI- coverage of reviews, pass/fail rates, exceptions handling


NEW QUESTION # 146
An organization is deploying an automated AI cybersecurity system. Which of the following would be the MOST effective strategy to minimize human error and improve overall security?

Answer: C

Explanation:
Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage-directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. "Ensure responsible use" is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.
References: AI Security Management™ (AAISM) Body of Knowledge: Model Development & Evaluation Controls; Data Selection and Representativeness; Operationalization to Reduce Human Error. AAISM Study Guide: Tuning Detection Systems with Historical Corpora; Alert Quality, Precision/Recall, and SOC Workflow Integration.


NEW QUESTION # 147
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