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A data science team needs to store daily churn predictions generated by a production MLflow model in a Delta Lake table. The solution must support historical analysis, allowing data scientists to compare predictions over time. Churn predictions are generated at most once per day. Which code snippet achieves this requirement with the lowest compute overhead and cost?
A
(preds.writeStream
.outputMode("overwrite")
.option("checkpointPath", "/_checkpoints/churn_preds")
.start("/preds/churn_preds"))
```_
(preds.writeStream
.outputMode("overwrite")
.option("checkpointPath", "/_checkpoints/churn_preds")
.start("/preds/churn_preds"))
```_
B
preds.write.mode("append").saveAsTable("churn_preds")
```_
preds.write.mode("append").saveAsTable("churn_preds")
```_
C
preds.write.format("delta").save("/preds/churn_preds")
```_
preds.write.format("delta").save("/preds/churn_preds")
```_
D
(preds.writeStream
.outputMode("append")
.option("checkpointPath", "/_checkpoints/churn_preds")
.table("churn_preds"))
```_
(preds.writeStream
.outputMode("append")
.option("checkpointPath", "/_checkpoints/churn_preds")
.table("churn_preds"))
```_
E
(preds.write
.format("delta")
.mode("overwrite")
.saveAsTable("churn_preds"))
```_
(preds.write
.format("delta")
.mode("overwrite")
.saveAsTable("churn_preds"))
```_