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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q236-Q241):

NEW QUESTION # 236
A bank's fraud detection AI model is trained on customer transaction data. A customer submits a data erasure request under privacy law, but the data also exists in training sets. Which of the following is the BEST approach if there is concern that complete data removal may reduce accuracy?

A. Suspend the AI model until all erasure requests have been processed.
B. Deny the request because legitimate purpose of use outweighs privacy rights.
C. Inform the customer that deletion from AI models is technically infeasible.
D. Update standard operating procedures to ensure that the customer's data is fully deleted.

Answer: D

Explanation:
Updating standard operating procedures to ensure the customer's data is fully deleted is the best approach because organizations must comply with applicable privacy and data erasure requirements. Operational procedures should address how data is removed from training datasets and associated AI processes while balancing model maintenance and retraining considerations.

NEW QUESTION # 237
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
B. Update governance policies based on lessons learned and ensure a feedback loop exists
C. Ensure the certificate of destruction is received and archived in line with data retention policies
D. Ensure backups are tested and access controls are recorded and audited to ensure compliance

Answer: C

Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.

NEW QUESTION # 238
An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data. Which of the following is the MOST effective data cleaning technique to improve model performance?

A. Applying statistical methods to address missing data and reduce bias
B. Tuning model hyperparameters to increase performance and accuracy
C. Deleting outlier data points to prevent unusual values impacting the model
D. Increasing the frequency of model retraining with the existing data set

Answer: A

Explanation:
The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction. Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.

NEW QUESTION # 239
Which of the following is the MOST effective way to mitigate the effects of outdated data in a large language model (LLM)?

A. Adding a new model trained with new information to the solution
B. Retraining the model periodically with an updated training dataset
C. Deploying retrieval-augmented generation (RAG) data to the model
D. Replacing the model with a more complex model in the solution

Answer: C

Explanation:
Retrieval-augmented generation (RAG) enables the LLM to access current external information at query time instead of relying solely on static training data. This is the most effective way to mitigate outdated knowledge because it provides dynamically updated and context-relevant information without requiring full model retraining.

NEW QUESTION # 240
Which of the following is the PRIMARY advantage of applying data minimization and federated learning methods within AI security management?

A. Ensuring personal data is only used by AI solutions when there is a legitimate business need
B. Facilitating the adaptation of data governance frameworks to AI use cases
C. Mitigating privacy concerns while fostering responsible use of AI
D. Streamlining AI security infrastructure configurations by unifying data repositories

Answer: C

Explanation:
Data minimization and federated learning reduce exposure of sensitive information by limiting the amount of centralized personal data used during AI training and processing. This helps protect privacy while still enabling effective and responsible AI model development and operation.

NEW QUESTION # 241
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