It would be helpful. Currently, the best way is just to read the table as a dataframe and use Pyspark dropDuplicates().

# Load the table
df = spark.table("yourtable")
 
# Drop duplicates based on the Id and Name columns
df = df.dropDuplicates(["Id", "Name"])
 
# Overwrite the original table with the resulting dataframe
df.write.mode("overwrite").saveAsTable("yourtable")