Your question is TF-IDF vs Word2Vec Usage. 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 are comparing two common text representation methods for an NLP system. One is a sparse term-based approach, and the other is a dense embedding approach that captures semantic similarity between words.
What are TF-IDF and Word2Vec, and when would you use them?