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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
AI Governance and Program Management	31%	- AI asset and data lifecycle management - AI security program development and management - AI governance frameworks and alignment with business objectives - Business continuity and incident response for AI systems - AI security policies, standards, and procedures
AI Technologies and Controls	38%	- AI security architecture and secure design principles - Security monitoring, testing, and continuous assurance for AI systems - Data management and protection controls for AI - AI lifecycle security: model selection, training, validation, and deployment - Privacy, ethics, trust, and safety controls
AI Risk Management	31%	- AI risk assessment, thresholds, and treatment strategies - AI threat and vulnerability management - Regulatory compliance and ethical considerations in AI - AI vendor and supply chain risk management

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which of the following is the PRIMARY benefit of having robust model versioning and rollback capabilities for AI models?

A. It aids in the preservation of forensic evidence for incident investigation.
B. It helps to prevent unauthorized modification of model training data.
C. It mitigates the risk associated with model degradation or failure.
D. It ensures the accuracy of AI model inventories and model cards.

Answer: C

Explanation:
Robust model versioning and rollback capabilities allow organizations to quickly restore a previously stable and validated model if a new version introduces degraded performance, instability, security issues, or operational failures. This directly mitigates the risk associated with model degradation or failure in production.

NEW QUESTION # 14
Which of the following datasets is MOST appropriate to use when tuning an AI model's hyperparameters to improve performance?

A. Training
B. Validation
C. Testing
D. Synthetic

Answer: B

Explanation:
The validation dataset is used to tune hyperparameters and optimize model performance while remaining separate from the training data, ensuring unbiased evaluation during the tuning process.

NEW QUESTION # 15
A hospital adopts an AI solution from an external vendor to help diagnose rare diseases. Which of the following BEST demonstrates the verification of security requirements for this technology?

A. Preview contractual clauses of mandatory security AI software update.
B. Integrate requirements for sensitive data aligned with healthcare regulations.
C. Require contractual clauses stipulating periodic information security reviews.
D. Perform regular in-depth vulnerability scans on the AI system.

Answer: B

Explanation:
Verification of security requirements for an AI used in diagnosis must focus on how sensitive health data (PHI) is protected in line with regulations such as HIPAA and similar healthcare laws.
Integrating explicit requirements for handling, storing, transmitting, and accessing sensitive data in accordance with healthcare regulations is a direct verification that the technology's security controls meet the hospital's legal and security obligations.

NEW QUESTION # 16
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. Streamlining AI security infrastructure configurations by unifying data repositories
C. Facilitating the adaptation of data governance frameworks to AI use cases
D. Mitigating privacy concerns while fostering responsible use of AI

Answer: D

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 # 17
A health services organization is developing a proprietary generative AI chatbot to assist patients with medical devices. Which of the following should be the organization's HIGHEST priority?

A. Maximizing neural network size
B. Tuning algorithms used in the AI model
C. Selecting the appropriate training data
D. Maximizing the amount of training data

Answer: C

Explanation:
AAISM prioritizes training data suitability--lawful sourcing, provenance, quality, representativeness, and safety--especially in health-related applications. The correctness and appropriateness of training data determine clinical safety, reduction of harmful outputs, and compliance with data protection/sector obligations. Larger models or more data do not compensate for inappropriate or low-quality datasets; tuning is secondary to ensuring the right data with rigorous curation, labeling quality, and guardrails aligned to patient safety requirements.

NEW QUESTION # 18
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