Neural Network Architectures: Recurrent & Sequence Models Questions
Comprehensive understanding of RNNs, LSTMs, GRUs, and Transformer architectures for sequential data. Understand the motivation for each (vanishing gradient problem, LSTM gates), attention mechanisms, self-attention, and multi-head attention. Know applications in NLP, time series, and other domains. Discuss Transformers in detail—they've revolutionized NLP and are crucial for generative AI.
MediumTechnical
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Describe how you would detect and respond to model drift in a deployed sequence model (e.g., customer support chatbot) where inputs gradually shift over months. Include monitoring metrics, data collection, retraining cadence, and automated triggers for rollback or retraining.
HardTechnical
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You're asked to migrate a legacy BiLSTM-based NER pipeline to a Transformer architecture. As the AI Engineer lead, create a migration plan covering model selection, data conversion, integration testing, performance regression tests, deployment strategy, and stakeholder communication.
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Explain the difference between autoregressive and non-autoregressive sequence models. Give examples of each, discuss where each is preferable, and highlight common challenges for non-autoregressive generation.
HardTechnical
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Explain how to use contrastive learning objectives with sequence encoders for representation learning (e.g., for retrieval of similar documents). Include how to form positive and negative pairs for sequential data and how to scale negatives effectively.
MediumTechnical
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A time-series forecasting Transformer is giving biased predictions during holiday seasons that are underrepresented in training. Propose concrete model and data interventions to correct seasonality bias, including augmentation, feature engineering, loss adjustments, and evaluation strategies.
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