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30 of 55. A data engineer is working on a num_df DataFrame and has a Python UDF defined as: def cube_func(val): return val * val * val Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?
A. spark.createDataFrame(cube_func("num")).show()
B. num_df.register("cube_func").select("num").show()
C. num_df.select(cube_func("num")).show()
D. spark.udf.register("cube_func", cube_func) num_df.selectExpr("cube_func(num)").show()
Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).
An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns: df1: employee_id INT, name STRING df2: emp_id INT, department STRING The engineer uses: result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner') What is the behaviour of the code snippet?
A. The code fails to execute because PySpark does not support joining DataFrames with a different structure
B. The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
C. The code fails to execute because the column names employee_id and emp_id do not match automatically
D. The code fails to execute because it must use on='employee_id' to specify the join column explicitly
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A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately. Which two characteristics of Apache Spark's execution model explain this behavior? Choose 2 answers:
A. Transformations are executed immediately to build the lineage graph.
B. The Spark engine optimizes the execution plan during the transformations, causing delays.
C. Only actions trigger the execution of the transformation pipeline.
D. The Spark engine requires manual intervention to start executing transformations.
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35 of 55. A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off. How can this be achieved?
A. By configuring the option checkpointLocation during writeStream.
B. By configuring the option recoveryLocation during SparkSession initialization.
C. By configuring the option checkpointLocation during readStream.
D. By configuring the option recoveryLocation during writeStream.
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13 of 55. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this: region_id region_name 10 North 12 East 14 West The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values. Which code fragment meets the requirements?
A. regions_dict = regions.select("region_id", "region_name").take(3)
B. regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
C. regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
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