Advanced Modeling Techniques Questions

Knowledge of state of the art model adaptation and augmentation approaches such as retrieval augmented generation, fine tuning, and transfer learning. Candidates should be able to explain when to use retrieval augmented approaches versus fine tuning, the trade offs in terms of compute cost, latency, update velocity and knowledge freshness, strategies for building and evaluating retrieval indices and embeddings, and how to leverage pretrained models with transfer learning for domain adaptation. Discuss practical concerns such as data requirements, catastrophic forgetting, regularization, and inference cost.