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NEW QUESTION # 169
A retailer ' s AI pricing engine recommends unusually large discounts in certain regions. An audit reveals that most historical sales data used for model training came from large urban areas, while smaller regions were represented by very few or no sales records. Which of the following is the MOST likely cause of this issue?
Answer: B
Explanation:
This scenario illustrates the problem of " Data Scarcity " or " Under-representation bias. " If the model is trained primarily on urban data, it will not have enough information to understand the unique economic or competitive dynamics of rural regions. As a result, when it encounters those regions, it may make erratic or extreme recommendations (like excessive discounts) because it is essentially " guessing " based on limited data. According to the AAIA™ manual, ensuring " Data Representativeness " across all demographic or geographic subgroups is a critical step in training. Without a balanced dataset, the model fails to generalize correctly to all operational environments, leading to significant financial loss and operational instability.
NEW QUESTION # 170
Which of the following techniques is MOST appropriate for model hyperparameter tuning?
Answer: D
Explanation:
Hyperparameter tuning is the process of finding the optimal settings (like learning rate or depth) for an AI model. " Bayesian optimization " is an efficient, automated technique that builds a probability model of the objective function to select the most promising hyperparameters to test next. Unlike simple " grid search, " it saves significant time and computational cost. F1 score analysis is a way to measure the result of the tuning, but it is not the tuning method itself. Bayesian optimization is favored in advanced AI development for its ability to find optimal configurations in complex, multi-dimensional spaces.
NEW QUESTION # 171
Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?
Answer: A
Explanation:
Bias in AI models is most commonly introduced through the training data. The AAIATM Study Guide highlights that to ensure fairness, auditors and developers must evaluate the diversity, representativeness, and quality of the data used to train the model.
"The greatest source of bias in AI comes from the training data. Reviewing and auditing this data is critical to ensuring that outputs do not disproportionately affect specific groups or skew results."
NEW QUESTION # 172
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
Answer: D
Explanation:
Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIATM Study Guide, labeled data includes input features along with the correct output, enabling the model to learn the mapping function accurately.
"In supervised learning, models learn from input-output pairs provided in the training data. This method enables predictive modeling tasks such as classification and regression." Unlabeled data (A) is used in unsupervised learning; clustered data (B) is a technique rather than a data type; and randomized data (D) refers to distribution strategy, not labeling. Hence, labeled data is the correct answer.
NEW QUESTION # 173
An insurance organization deployed an AI tool for assigning customer risk levels. An IS auditor discovers that the learning algorithm is vulnerable to adversarial attacks. Which of the following is the BEST course of action?
Answer: D
Explanation:
Adversarial attacks involve "manipulated inputs" (poisoning or evasion) designed to trick a model into making incorrect decisions (e.g., assigning a high-risk driver a low-risk premium). To mitigate this, the auditor must "Validate the process for handling manipulated inputs." This includes checking for robust input validation, anomaly detection on incoming data, and "adversarial training" where the model is intentionally exposed to these attacks during development to build resilience.
NEW QUESTION # 174
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