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NEW QUESTION # 44
Which of the following AI data management techniques involves creating validation and test data?
Answer: A
Explanation:
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.
NEW QUESTION # 45
When evaluating a new AI tool for intrusion prevention, which of the following is the MOST important consideration to ensure the tool fits within the existing program architecture?
Answer: C
Explanation:
The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization's established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to--and can be verified against--the control objectives that define the program's intended outcomes.
NEW QUESTION # 46
An attacker modifies portions of training data over time within an organization's developing AI system. If this manipulation goes undetected, which of the following is the MOST likely risk?
Answer: B
Explanation:
Gradual manipulation of training data is a form of data poisoning, which directly affects how the model learns patterns. This leads to skewed or biased outputs, making biased decision-making the most likely and immediate risk.
NEW QUESTION # 47
When an AI impact assessment reveals biased training data, what is the MOST effective method to ensure regulatory compliance?
Answer: D
NEW QUESTION # 48
Which of the following BEST describes the role of transparency in AI?
Answer: B
Explanation:
Transparency in AI is a governance principle requiring that systems be explainable to stakeholders in ways that are understandable and meaningful, enabling clear articulation of how decisions were reached and why. Within an AI program, transparency supports accountability, auditability, and trust by ensuring that reasons for decisions can be communicated and scrutinized. Option C reflects this definition by focusing on intelligible, logical explanations of system behavior and decision rationale.
NEW QUESTION # 49
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