Your question is Prevent Churn Model Data Leakage. 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 prevent data leakage when engineering features for a predictive churn model?
Explain how you would define the prediction timestamp, construct point-in-time-correct features, split the data, and validate the pipeline. Include production safeguards for future data availability, target-derived fields, customer-level overlap, and training-serving skew. Provide concise Python demonstrating leakage-safe preprocessing and evaluation without using information unavailable at prediction time.