My Experience

I recently appeared for an NLP online exam that tested both foundational concepts and applied problem-solving skills. The paper included a mix of multiple-choice questions, short case-based scenarios, and theoretical concepts from both classical NLP and modern deep learning approaches.

Preparation was key — I revised essential topics like tokenisation, embeddings, attention mechanisms, and evaluation metrics. Time management was critical, so I followed a structured approach: attempt easy questions first, mark the trickier ones for review, and leave time for calculations at the end.

In this post, I’ll share the areas that helped me score better, common pitfalls to avoid, and resources I recommend for anyone preparing for an NLP exam.

Key Learnings

• Understand the difference between stemming and lemmatisation — and when to use each.
• Know the trade-offs between Bag-of-Words, TF-IDF, and contextual embeddings like BERT.
• Be comfortable with macro vs micro averaging in classification metrics.
• Brush up on smoothing techniques in language models.
• Practice interpreting attention maps and model outputs.

Recommended Resources

• Jurafsky & Martin’s “Speech and Language Processing” (key chapters on language models & sequence-to-sequence models).
• Hugging Face’s free online course for Transformers and fine-tuning.
• Kaggle datasets for practising NLP tasks like sentiment analysis and named entity recognition.