Your question is TF-IDF vs Word Embeddings. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You're working on a text modeling problem and need to choose how to represent language for a downstream NLP task. Two common options are TF-IDF features and word embeddings, and the trade-offs affect both model quality and implementation.
How would you explain the difference between TF-IDF and word embeddings?