Your question is Feature Selection in High Dimensions. 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 analyzing a high-dimensional system dataset and want to understand which variables have meaningful relationships with an outcome or with each other. Many features are correlated, noisy, or redundant, so naive interpretation can be misleading.
How do you handle feature selection when interpreting relationships in highly dimensional system data?