Answer: ```python preds.write.mode("append").saveAsTable("churn_preds") ```

### Correct Choice **B. preds.write.mode("append").saveAsTable("churn_preds")** ### Why it's the best fit: 1. **Cost-Efficiency:** For data that is processed only once per day, a batch write is significantly cheaper than Structured Streaming. A batch job performs the write and then allows the compute resources to shut down immediately, whereas a continuous stream (even if idle) or the overhead of managing streaming checkpoints for daily data is unnecessary. 2. **Historical Integrity:** The `append` mode ensures that each day's predictions are added as new rows to the existing Delta table. This preserves historical data, enabling comparisons across time. 3. **Managed Delta Lake Table:** On Databricks, `saveAsTable` defaults to the Delta format. This provides ACID transactions and time-travel capabilities out of the box. ### Analysis of Other Options: * **Option C:** The default write mode for Spark is `errorIfExists`. This snippet would fail on the second day because the path already exists. * **Option E:** Using `overwrite` replaces the entire dataset every day, making it impossible to perform the historical comparisons requested by the team. * **Options A & D:** Using Structured Streaming for data that arrives once a day is inefficient. Even if using `Trigger.AvailableNow`, the simplicity and lower overhead of a batch `append` make it the preferred choice for this specific frequency. Additionally, Option A uses `overwrite`, which destroys the history the team requires.

Author: LeetQuiz Editorial Team