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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 (Q47-Q52):

NEW QUESTION # 47
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?

A. Identifying hallucinations returned by AI models
B. Establishing clear lines of responsibility for AI model outputs
C. Determining the aggregate risk of the system
D. Explaining the overall benefit of the system to stakeholders

Answer: C

Explanation:
When multiple AI models interact in a connected supply chain system, risks can propagate and compound across models. Assessing the overall, aggregate risk - including errors, biases, or failures from individual models - is challenging but critical for ensuring reliable and safe decision- making.

NEW QUESTION # 48
A retailer's AI-driven dynamic pricing system failed after its feature store was corrupted, resulting in all prices being set to zero. The data source has been isolated, and management demands immediate recovery. Which of the following is the MOST appropriate course of action to restore operations while minimizing impact?

A. Keep the system offline and conduct a full forensic investigation.
B. Revert to the previous AI model and validate backup of the feature store.
C. Require customer service to manually price and approve orders until the AI system is fully restored.
D. Failover to a secondary environment and retrain the model with recent data.

Answer: B

Explanation:
Reverting to a previously stable model and validating a clean backup of the feature store enables rapid restoration of normal operations while ensuring data integrity. This minimizes business disruption and avoids propagating corrupted data into the system.

NEW QUESTION # 49
An e-commerce platform has been subjected to false product image submissions and reviews, as well as fake search and purchase histories. Which of the following is the information security team's BEST course of action in this situation?

A. Block users who submitted the false information.
B. Implement data monitoring tools to detect false data.
C. Review the pre-processing tools.
D. Verify data provenance.

Answer: D

Explanation:
Verifying data provenance is the best course of action because it helps confirm the origin, authenticity, and integrity of submitted images, reviews, and behavioral data. This directly addresses the risk of malicious or fabricated data contaminating the platform and influencing AI- driven recommendations or analytics.

NEW QUESTION # 50
An AI system that supports critical processes has deviated from expected performance and is producing biased outcomes. Which of the following is the BEST course of action?

A. Conduct audits of the data and the model
B. Activate the model kill switch
C. Retrain the model with a new and expanded dataset
D. Perform a root cause analysis to identify mitigation steps

Answer: D

Explanation:
AAISM directs that when harmful or biased behavior is observed in a production AI system, the organization should enter a formal incident/variance handling workflow that begins with root cause analysis (RCA) to identify the source of deviation (data drift, concept drift, feature leakage, pipeline changes, control failures) and determine proportionate risk treatments. Immediate retraining (Option A) without RCA risks reinforcing the same bias; audits (Option C) are key activities within RCA rather than the action that frames the response; a kill switch (Option D) is reserved for conditions where risk exceeds the defined tolerances and immediate harm prevention is required.
References: AI Security Management™ (AAISM) Body of Knowledge - Incident Response & Post-Incident Improvement; Model Risk Treatment & Drift Management; Bias Detection and Remediation Governance.

NEW QUESTION # 51
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?

A. Utilizing unstructured data sets
B. Diversifying the model training data
C. Adopting a more simplified model
D. Securing the model training data

Answer: B

Explanation:
AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.

NEW QUESTION # 52
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