Which code block is best suited to save predictions to a Delta Lake table with minimal compute costs?

The best choice for saving predictions to a Delta Lake table with minimal compute costs is to write the DataFrame directly to a Delta table using the append mode. The statement preds.write.mode("append").saveAsTable("churn_preds") allows the DataFrame preds to be added to the existing table without incurring the overhead associated with data reprocessing or duplication of data that could occur with other write operations.

Using append mode is particularly efficient because it allows new predictions to be directly added to the existing entries in the Delta table, rather than rewriting the entire dataset, which helps minimize compute resources and time. This approach capitalizes on Delta Lake's capabilities to manage incremental updates efficiently.

By choosing this method, you ensure that only the new data is written to the table, making it a cost-effective and performance-efficient solution for updating your Delta Lake with predictions.