GENERATIVE-AI-LEADER Question #78: Real Exam Question with Answer & Explanation

Question

A global travel booking platform named VistaVoyage is developing a generative AI system to identify payment fraud across about 45 million reservations each day. The team is concerned that adversaries may make small tweaks to inputs so the model incorrectly treats fraudulent behavior as legitimate. At what point in the machine learning lifecycle should robust protections against these adversarial tactics be established to preserve security?

Options

• ALimited to the business requirements and initial threat modeling stage
• BHandled mostly with input sanitation and validation in Dataflow pipelines before training or serving
• CIt should be continuous with robustness techniques embedded during model training and
• DExclusively when the model is released to production

Explanation

Adversarial robustness needs to be designed into the model from the start and then sustained in production. During training you can harden models with adversarial training, robust data augmentation, regularization, and careful evaluation against adversarial and out of distribution test sets. In production you should continuously monitor for drift, anomalies, and suspicious input patterns and you should feed incidents back into retraining so the system improves over time. This lifecycle approach ensures protections evolve with attacker tactics and with data and model

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