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Which of the following code blocks applies the Python function to_limit on column predError in table transactionsDf, returning a DataFrame with columns transactionId and result?
Correct Answer: A
Explanation spark.udf.register("LIMIT_FCN", to_limit) spark.sql("SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf") Correct! First, you have to register to_limit as UDF to use it in a sql statement. Then, you can use it under the LIMIT_FCN name, correctly naming the resulting column result. spark.udf.register(to_limit, "LIMIT_FCN") spark.sql("SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf") No. In this answer, the arguments to spark.udf.register are flipped. spark.udf.register("LIMIT_FCN", to_limit) spark.sql("SELECT transactionId, to_limit(predError) AS result FROM transactionsDf") Wrong, this answer does not use the registered LIMIT_FCN in the sql statement, but tries to access the to_limit method directly. This will fail, since Spark cannot access it. spark.sql("SELECT transactionId, udf(to_limit(predError)) AS result FROM transactionsDf") Incorrect, there is no udf method in Spark's SQL. spark.udf.register("LIMIT_FCN", to_limit) spark.sql("SELECT transactionId, LIMIT_FCN(predError) FROM transactionsDf AS result") False. In this answer, the column that results from applying the UDF is not correctly renamed to result. Static notebook | Dynamic notebook: See test 3
