Natural Language Processing Fundamentals Questions

Foundational knowledge of Natural Language Processing including how text is represented and processed, common tasks, model classes, and practical tooling. Core concepts include tokenization and subword segmentation, embedding representations and word vector methods such as Word2Vec and GloVe, attention mechanisms, sequence to sequence models, recurrent neural networks, and transformer based architectures. Common tasks to understand include text classification, sentiment analysis, named entity recognition, and machine translation. Candidates should be able to explain how pretrained transformer models are used conceptually, trade offs between model types, basic training and evaluation approaches for language tasks, and practical experience or familiarity with common libraries and toolkits used in the field.