Your question is Handling Multicollinearity. 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).
How do you handle multi-collinearity in a linear regression model?
Explain how you would detect multicollinearity, distinguish its effects on coefficient interpretation from its effects on prediction, and choose among feature removal, feature transformation, dimensionality reduction, and regularization. Include how you would validate that the selected treatment improves the model without introducing data leakage.