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NEW QUESTION # 362
Which of the following datasets is used to tune hyperparameters?
Answer: D
Explanation:
Per AAISM's ML lifecycle controls, hyperparameter tuning is performed on the validation set, reserving the test set strictly for final, unbiased performance estimation. The training set is used to fit parameters; the validation set guides model selection and hyperparameter optimization; the test set is untouched until the end to prevent leakage and optimistic bias. "Configuration" is not a dataset type in the lifecycle split.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Development Controls- Data Splitting and Evaluation Integrity* AAISM Study Guide: Overfitting Avoidance; Validation vs. Test Separation; Leakage Prevention* AAISM Mapping to Standards: Evaluation Integrity-Hold-out Protocols and Tuning Practices
NEW QUESTION # 363
A data scientist creating categories and training an algorithm on large data sets is performing which learning technique?
Answer: C
Explanation:
AAISM identifies supervised learning as involving:
- labeled categories
- ground-truth datasets
- model training with known outcomes
This aligns exactly with categorizing data and training on labeled datasets.
NEW QUESTION # 364
In a new supply chain management system, AI models used by participating parties are interactively connected to generate advice in support of management decision making. Which of the following is the GREATEST challenge related to this architecture?
Answer: B
Explanation:
The AAISM governance framework notes that in multi-party AI ecosystems, the greatest challenge is ensuring clear accountability for AI outputs. When models from different parties interact, responsibility for errors, bias, or harmful recommendations can be unclear, leading to disputes and compliance gaps. While aggregate risk assessment and error identification are significant, they are secondary to the fundamental governance requirement of establishing transparent lines of responsibility. Without defined accountability, no stakeholder can reliably manage or mitigate risks. Therefore, the greatest challenge in such a distributed architecture is responsibility for AI outputs.
References:
AAISM Study Guide - AI Governance and Program Management (Accountability in Multi-Party Systems) ISACA AI Governance Guidance - Roles and Responsibilities in AI Collaboration
NEW QUESTION # 365
During red-team testing of an AI system used to make lending decisions, which of the following techniques BEST simulates a data poisoning attack?
Answer: B
Explanation:
AAISM defines data poisoning as the intentional manipulation of training data so that the learned model behaves incorrectly (e.g., skewed lending approvals/denials) while appearing valid. The correct simulation in red-team exercises is to corrupt or seed the training set with adversarial examples or mislabeled records to induce biased or erroneous decision boundaries. Encrypting inputs (A) is unrelated; output noise (B) describes perturbation of predictions, not training; model weight theft (C) is model extraction, not poisoning.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML Threats; Data Poisoning and Training-Time Attacks. AAISM Study Guide - Red-Team Methods for AI; Poisoning vs.
Evasion vs. Model Extraction; Controls and Testing for Safety-Critical Decisions.
NEW QUESTION # 366
Which of the following AI data management techniques involves creating validation and test data?
Answer: D
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 # 367
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