The ISACA AAISM Web-Based Practice Exam

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ISACA AAISM Exam copyright Topics:

TopicDetails
Topic 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.
Topic 2
  • 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 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 (Q240-Q245):

NEW QUESTION # 240
A CISO must provide KPIs for the organization's newly deployed AI chatbot. Which metrics are BEST?

Answer: C

Explanation:
AAISM recommends that AI KPIs should emphasize:
* Error rates - measure correctness and reliability
* Bias detection metrics - assess fairness and harm risk
These are the core governance KPIs for AI systems interacting with end users.
Response time (A) is a performance metric but not an AI governance KPI. Customer retention (C) is business- focused. F1 score (D) is useful but not as critical as bias monitoring.
References: AAISM Study Guide - AI Performance, Fairness & Governance Metrics.


NEW QUESTION # 241
Which of the following BEST represents a combination of quantitative and qualitative metrics that can be used to comprehensively evaluate AI transparency?

Answer: D

Explanation:
The AAISM governance framework emphasizes that AI transparency cannot be evaluated using only technical statistics; it requires a combination of quantitative and qualitative metrics. The best pairing is ethical impact assessments (qualitative) with user feedback metrics (quantitative and perception-based). Availability and accuracy metrics measure performance, not transparency. Explainability reports and bias metrics are useful but still technical and limited. Comprehensive evaluation of transparency requires consideration of ethical dimensions and stakeholder perspectives, which is achieved through ethical impact analysis and user feedback.
References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Accountability) ISACA AI Security Management - Measuring Ethical AI Practices


NEW QUESTION # 242
Which strategy is MOST effective for penetration testers assessing an AI model against membership inference attacks?

Answer: C

Explanation:
AAISM specifies that membership inference attacks often exploit unusually high confidence scores when the model encounters data points used during training. Penetration testers identify vulnerability by analyzing model confidence behavior across known and unknown samples.
Synthetic data (A) does not test inference leakage. Disabling logs (C) removes evidence and reduces visibility. Test-set accuracy (D) is unrelated.
References: AAISM Study Guide - AI Privacy Attacks; Membership Inference Testing Techniques.


NEW QUESTION # 243
In a new supply chain management system, AI models used by participating parties are interactively connected to generate advice in support of management decision making. Which of the following is the GREATEST challenge related to this architecture?

Answer: C

Explanation:
The AAISM governance framework notes that in multi-party AI ecosystems, the greatest challenge is ensuring clear accountability for AI outputs. When models from different parties interact, responsibility for errors, bias, or harmful recommendations can be unclear, leading to disputes and compliance gaps. While aggregate risk assessment and error identification are significant, they are secondary to the fundamental governance requirement of establishing transparent lines of responsibility. Without defined accountability, no stakeholder can reliably manage or mitigate risks. Therefore, the greatest challenge in such a distributed architecture is responsibility for AI outputs.
References:
AAISM Study Guide - AI Governance and Program Management (Accountability in Multi-Party Systems) ISACA AI Governance Guidance - Roles and Responsibilities in AI Collaboration


NEW QUESTION # 244
Which of the following security framework elements BEST helps to safeguard the integrity of outputs generated by AI algorithms?

Answer: C

Explanation:
According to AAISM technical controls, the element of security frameworks that directly safeguards output integrity is ensuring that bias-related risks are maintained within acceptable ranges. This ensures that AI results remain consistent, fair, and reliable, preserving trust in outputs. Ethical awareness, architectural disclosure, and legal responsibilities are governance practices but do not directly secure output integrity.
Output integrity is primarily protected through bias management and ongoing evaluation of fairness and accuracy.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Integrity of AI Outputs) AI Security Management Study Guide - Bias and Output Integrity Controls


NEW QUESTION # 245
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