Your question is Ridge vs Lasso Regularization. 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).
What is the exact mathematical difference between Ridge (L2) and Lasso (L1) regression regularization? Under what specific data conditions would you prefer one over the other?
Explain the objective functions, coefficient behavior, impact of correlated and irrelevant features, and how you would validate the choice in practice. Include a production-quality implementation that compares both methods using cross-validation.