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| Section | Weight | Objectives |
|---|---|---|
| AI Governance and Program Management | 31% | - AI Strategy and Policy
|
| AI Risk Management | 31% | - AI Risk Assessment
|
| AI Technologies and Controls | 38% | - Data and Model Controls
|
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NEW QUESTION # 94
Which of the following BEST ensures AI components are validated during disaster recovery testing?
Answer: C
Explanation:
AAISM states that AI disaster recovery testing must validate that models behave correctly during failover.
The only option that tests actual operational continuity of AI components is:
# monitoring model performance during failover
This validates stability, functionality, and resilience under disaster conditions.
Options A, B, and C test isolated scenarios but do not validate end-to-end AI operational continuity.
References: AAISM Study Guide - AI Resilience & Disaster Recovery Testing.
NEW QUESTION # 95
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?
Answer: D
Explanation:
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces
NEW QUESTION # 96
Which of the following is the BEST approach for improving the robustness of an AI system in production?
Answer: D
Explanation:
Automated retraining helps maintain AI system robustness by adapting the model to changing data patterns, environmental conditions, and performance drift over time. This enables the system to sustain reliable and accurate behavior in production environments.
NEW QUESTION # 97
Which of the following is the MOST effective way to identify deepfakes generated by AI?
Answer: C
Explanation:
Analyzing biometric inconsistencies is the most effective method because deepfakes often fail to perfectly replicate natural human physiological and behavioral characteristics such as facial movements, blinking patterns, voice characteristics, or lip synchronization. These inconsistencies provide strong indicators of AI-generated manipulation.
NEW QUESTION # 98
An organization is deploying an automated AI cybersecurity system. Which of the following would be the MOST effective strategy to minimize human error and improve overall security?
Answer: C
Explanation:
Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage--directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. "Ensure responsible use" is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.
NEW QUESTION # 99
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