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A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?
Correct Answer: C
Comprehensive and Detailed Explanation From Exact Extract: Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables." Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." Exact extract: "MERGE INTO updates and inserts only the rows that changed." Overwriting the table nightly makes it difficult to isolate just the changed rows. With CDF enabled, if you update the table using MERGE so only changed rows are touched, you can read exactly those changed rows from CDF for the last 24 hours and score only them, which is simpler and more efficient. Reference: