100% Pass 2026 Professional ISACA Test AAISM Questions Pdf

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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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q216-Q221):

NEW QUESTION # 216
Secure aggregation enhances the security of federated learning systems by:

Answer: A

Explanation:
Secure aggregation cryptographically aggregates client updates so that the server learns only the sum
/aggregate, not any single client's update. Properly implemented, the server cannot recover individual contributions-even if compromised-thereby preserving client confidentiality. Option C (encryption in transit) is insufficient because decryption at the server reveals updates; Option A is procedural, not cryptographic; Option B (differential privacy) is a separate technique and not the defining property of secure aggregation.
References: AAISM Body of Knowledge: Privacy-Preserving ML-Federated Learning and Secure Aggregation; AAISM Study Guide: Threat Models for Aggregation Servers and Confidentiality Guarantees.


NEW QUESTION # 217
An organization is reviewing an AI application to determine whether it is still needed. Engineers have been asked to analyze the number of incorrect predictions against the total number of predictions made. Which of the following is this an example of?

Answer: B

Explanation:
AAISM guidance identifies metrics like error rate versus total predictions as a key performance indicator (KPI) for evaluating AI model effectiveness. KPIs provide measurable values to assess performance against objectives. Model validation is broader and occurs prior to production use, testing the model against predefined standards. Control self-assessment relates to governance processes, not predictive accuracy.
Explainable decision-making refers to interpretability, not error-rate evaluation. Thus, analyzing incorrect predictions against total predictions is a performance measure, making it a KPI.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Performance Metrics and KPIs) AI Security Management Study Guide - Accuracy and Error Metrics


NEW QUESTION # 218
Which of the following is the MOST important consideration when an organization is adopting generative AI for personalized advertising?

Answer: C

Explanation:
In AI program decisions that process personal data for targeted or personalized advertising, the primary governance obligation is to ensure regulatory compliance with data protection and profiling requirements.
AAISM emphasizes that when AI use cases involve personal data, regulatory risk is paramount because unlawful collection, consent failures, opaque profiling, or non-compliant automated decision-making can trigger enforcement actions, fines, mandated remediation, and restrictions on processing-risks that outweigh commercial or reputational impacts in both likelihood and severity for this use case. AAISM guidance requires upfront legal basis assessment, purpose limitation, data minimization, transparency, user rights enablement, and DPIA/AI impact assessments before deployment, making regulatory conformance the first- order decision criterion for personalized advertising with generative AI.
References:* AI Security Management (AAISM) Body of Knowledge: AI Governance-Compliance-by- design, lawful basis and purpose limitation for profiling; Impact assessments and consent governance.* AI Security Management Study Guide: Personal-data use cases, high-risk processing controls, regulatory risk prioritization in AI adoption decisions.


NEW QUESTION # 219
Which of the following is the MOST effective way to prevent a model inversion attack?

Answer: C

Explanation:
AAISM identifies differential privacy as the primary mitigation technique against model inversion attacks, which attempt to reconstruct sensitive training data by probing model outputs.
Pseudonymization (B) and minimization (D) reduce exposure but do not prevent inversion. Output monitoring (A) detects anomalies but doesn't block reconstruction.
References: AAISM Study Guide - Privacy Attacks and Defenses; Differential Privacy.


NEW QUESTION # 220
Which of the following BEST describes the role of transparency in AI?

Answer: D

Explanation:
Transparency in AI is a governance principle requiring that systems be explainable to stakeholders in ways that are understandable and meaningful, enabling clear articulation of how decisions were reached and why.
Within an AI program, transparency supports accountability, auditability, and trust by ensuring that reasons for decisions can be communicated and scrutinized. Option C reflects this definition by focusing on intelligible, logical explanations of system behavior and decision rationale.
Option A is a narrow technique (model-specific interpretability for decision trees) and does not capture transparency as a broad governance requirement. Option B conflates transparency with full public disclosure; transparency does not require making all artifacts openly available. Option D is persuasion/advocacy, not transparency.
References: AI Security Management (AAISM) Body of Knowledge: "AI Governance-Transparency and Explainability," "Accountability and Assurance"; AAISM Study Guide: "Explainability Objectives and Stakeholder Communication," "Documentation for Decision Rationale."


NEW QUESTION # 221
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