Q1
Walk me through how you would design and implement a named entity recognition (NER) system for a production environment. What trade-offs would you consider between using a pre-trained transformer model versus fine-tuning one on domain-specific data?
Why they ask this:* They want to assess your understanding of NLP architecture decisions, model selection, fine-tuning strategies, and practical considerations for production systems.
Q2
Explain the difference between attention mechanisms in transformers and recurrent neural networks. How would you decide which architecture to use for a sequence-to-sequence task like machine translation?
Why they ask this:* They're evaluating your grasp of fundamental deep learning concepts, architectural trade-offs, and your ability to justify technical choices based on task requirements.
Q3
Describe your experience with data preprocessing and augmentation for NLP tasks. How do you handle class imbalance, misspellings, or multilingual text in your pipelines?
Why they ask this:* They want to know if you understand that real-world NLP data is messy and whether you have practical strategies for improving model robustness and generalization.
Q4
Walk me through your approach to evaluating an NLP model beyond standard metrics like accuracy or F1-score. What additional techniques would you use to ensure a model performs well in production?