how lazy is DeltaTable.toDF (Spark and delta.io)?

Question

Suppose you do something like

import io.delta.tables._

val deltaTable = DeltaTable.forPath(spark, "...")

deltaTable.updateExpr(
  "column_name = value",
  Map("updated_column" -> "'new_value'")

val df = deltaTable.toDF

Will df re-read the underlying Delta table contents on demand whenever accessed (e.g., df.count()), post-update? Such that deltaTable.toDF is effectively equivalent to spark.read.format("delta").load(path)?

Or will it re-apply the series of updates whenever df is accessed?