Advanced Growth Analytics Masterclass Practice Test 2026 | E

When evaluating a customer churn prediction model, which metric is most appropriate if the cost of a false negative (failing to identify a churner) is significantly higher than the cost of a false positive?

A growth team is building a model to estimate Customer Lifetime Value (CLV) for a subscription service. Which approach is best suited for capturing the underlying customer behavior patterns like changing engagement over time?

In the context of A/B testing for a new feature, a statistically significant result (p-value < 0.05) was observed for a primary metric. However, a machine learning model trained on user-level data finds no interaction effect between the user's existing engagement level and the treatment. What does this finding most strongly suggest?