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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD $399 |
| Available Languages: | Spanish, English |
| Exam Duration: | 150 minutes |
| Real Exam Qty: | 90 |
| Exam Format: | Multiple Choice, Scenario-based Questions |
| Related Certifications: | ISACA Advanced in AI Security Management (AAISM) |
| Passing Score: | 450/800 |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Online remote proctored exam or test center exam |
| Pre Condition: | Candidates must hold an active CISM or CISSP certification. |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline |
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NEW QUESTION # 207
The PRIMARY benefit of implementing moderation controls in generative AI applications is that it can:
Answer: A
Explanation:
AAISM materials identify the primary benefit of moderation controls in generative AI systems as their ability to filter out harmful, offensive, or inappropriate content before it is delivered to users.
This safeguards organizational reputation, compliance, and user trust. While moderation may indirectly support compliance with privacy requirements, its main function is ensuring that outputs align with ethical and safety standards. Moderation does not enhance creativity or response speed. Its primary value is in controlling the quality of generated outputs by blocking harmful content.
NEW QUESTION # 208
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?
Answer: C
Explanation:
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
- proof of lawful data disposal
- auditability
- regulatory compliance
- defensibility during inspections
NEW QUESTION # 209
Embedding unique identifiers into AI models would BEST help with:
Answer: B
Explanation:
The AAISM framework explains that embedding unique identifiers-such as digital watermarks or model fingerprints-enables organizations to trace and verify model provenance. This technique is used for tracking ownership and intellectual property rights over models, particularly when sharing, licensing, or distributing AI systems. While identifiers may support certain security functions, their primary control objective is ownership verification, not preventing access, bias removal, or adversarial detection. The correct alignment with AAISM controls is tracking ownership.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Model Provenance and Watermarking) AI Security Management Study Guide - Ownership and Accountability of Models
NEW QUESTION # 210
Which of the following is the MOST important factor to consider when selecting industry frameworks to align organizational AI governance with business objectives?
Answer: D
Explanation:
According to AAISM governance principles, the risk appetite of the organization is the most important factor in selecting appropriate frameworks for AI governance. Risk appetite defines the level of risk an organization is willing to accept in pursuit of its objectives, ensuring frameworks are aligned with strategic goals. Risk tolerance and thresholds are operational measures derived from appetite, and the risk register is a documentation tool. The foundational consideration for framework alignment is the organization's risk appetite.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Risk Appetite in Governance Alignment) AI Security Management Study Guide - Framework Selection and Business Strategy
NEW QUESTION # 211
An automotive manufacturer uses AI-enabled sensors on machinery to monitor variables such as vibration, temperature, and pressure. Which of the following BEST demonstrates how this approach contributes to operational resilience?
Answer: D
Explanation:
AAISM highlights that AI-enabled predictive maintenance improves operational resilience by using real-time sensor monitoring to schedule repairs based on actual conditions rather than fixed schedules. This prevents unexpected breakdowns, reduces downtime, and ensures continuity of operations. Regular maintenance based on recommendations is static and may not reflect real conditions. Manual reviews are slow and inefficient.
Full automation of repairs without human oversight is not realistic or safe in critical manufacturing. The approach that best demonstrates resilience is real-time condition-based repair scheduling.
References:
AAISM Study Guide - AI Risk Management (Operational Resilience and Predictive Maintenance) ISACA AI Security Management - AI for Critical Infrastructure Reliability
NEW QUESTION # 212
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