Create and Invoke Pandas UDF. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   What is a potential overhead of using Pandas UDFs compared to standard UDFs?

   • AThey require a separate cluster for Python execution.
   • BThey require more memory because they load entire datasets into memory at once.
   • CThey are limited to running on a single executor.
   • DThey have higher serialization overhead due to converting data to pandas objects.
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2. 2foundation · easy

   What is the purpose of the return type argument in the @pandas_udf decorator?

   • AIt specifies the Python type of the return value of the function.
   • BIt defines the Spark SQL data type of the resulting column.
   • CIt determines the number of rows the UDF will return.
   • DIt specifies the pandas version to use for the UDF.
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4. 3application · medium

   A data engineer needs to apply a custom transformation to each group of a DataFrame. The transformation takes all rows in a group and returns a new DataFrame with potentially a different number of rows. Which Pandas UDF type should they use?

   • AGROUPED_MAP
   • BSCALAR
   • CGROUPED_AGG
   • DA regular UDF
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5. 4foundation · easy

   You need to apply a custom aggregation that returns a single value per group (e.g., the range of values in a group). Which Pandas UDF type should you use?

   • AGROUPED_MAP
   • BGROUPED_AGG
   • CITER
   • DSCALAR
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6. 5foundation · easy

   Which Pandas UDF type is appropriate for applying a function that returns one value per row, such as calculating the length of a string column?

   • AGROUPED_MAP
   • BGROUPED_AGG
   • CTABLE
   • DSCALAR
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