Your question is XGBoost vs Deep Learning Tabular. 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 comparing candidate models for a supervised learning problem on tabular behavioral data. You want to understand when a tree ensemble is the better choice and when a deep learning model is worth the added complexity.
How do tree-based ensemble methods like XGBoost compare to deep learning models for tabular behavioral data?