Exam DSA-C03 Topic 1 Question 143 Discussion

Actual exam question for Snowflake's DSA-C03 exam
Question #: 143
Topic #: 1

You are working with a Snowflake table 'CUSTOMER TRANSACTIONS containing customer IDs, transaction dates, and transaction amounts. You need to identify customers who are likely to churn (stop making transactions) in the next month using a supervised learning model. Which of the following strategies would be MOST appropriate to define the target variable (churned vs. not churned) and create features for this churn prediction problem, suitable for a Snowflake-based machine learning pipeline?

A. Define churn as customers with zero transactions in the last month. Create features like average transaction amount over the past year, number of transactions in the past month, and recency (time since the last transaction). B. Define churn as customers with a significant decrease (e.g., 50%) in transaction amounts compared to the previous month. Create features based on demographic data and customer segmentation information, joined from other Snowflake tables. C. Define churn based on a fixed threshold of total transaction value over a predefined period. Feature Engineering should purely consist of time series decomposition using Snowflake's built-in functions. D. Define churn as customers who haven't made a transaction in the past 6 months. Create a single feature representing the total number of transactions the customer has ever made. E. Define churn as customers with no transactions in the next month (the prediction target). Create features including: Recency (days since last transaction), Frequency (number of transactions in the past 3 months), Monetary Value (average transaction amount over the past 3 months), and trend of transaction amounts (using linear regression slope over the past 6 months).

Suggested Answer: E Vote an answer

Option E is the most appropriate strategy. Defining churn as the absence of transactions in the next month allows for building a predictive model. The features Recency, Frequency, Monetary Value (RFM), and the trend of transaction amounts provide a comprehensive view of the customer's transaction behavior, capturing both the current activity and the recent trend. Option A's definition of churn is based on the past month, which is not predictive. Option B's definition of churn is too sensitive to temporary fluctuations. Option C's approach limits the value of featurization. Option D lacks depth in featurization.

by Jo at Sep 14, 2025, 11:41 AM

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