Transform Data Using SQL and Snowpark DEA-C02 Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A data engineer is writing a Snowpark Python transformation that joins two large DataFrames on a high-cardinality key. The job is running slowly due to excessive shuffling. Which Snowpark DataFrame method should the engineer use to reduce the shuffle cost?

   • AUse df.cache() on both DataFrames before joining.
   • BUse df.collect() on the smaller DataFrame and then perform the join in Python.
   • CUse df.join() with the broadcast hint on the smaller DataFrame.
   • DCall df.repartition() on the join key before the join.
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2. 12application · medium

   A data engineer is writing a Snowpark Python script that reads from a table, applies a filter, and writes the result to a new table. The script will be run manually by analysts. Which Snowpark DataFrame method should the engineer use to execute the transformation?

   • Adf.show()
   • Bdf.collect()
   • Cdf.toPandas()
   • Ddf.write.save_as_table()
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4. 13application · medium

   A data engineer needs to create a UDF that returns a table of results, such as splitting a string into multiple rows. Which type of UDF should they create?

   • AVectorized UDF
   • BSQL UDF
   • CTabular UDF
   • DScalar UDF
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5. 14foundation · easy

   Which SQL command is used to create a user-defined function in Snowflake?

   • ACREATE TABLE
   • BCREATE FUNCTION
   • CCREATE VIEW
   • DCREATE PROCEDURE
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6. 15foundation · easy

   Which Snowflake feature improves query performance by physically organizing data in a table based on one or more columns?

   • AClustering keys
   • BSearch optimization service
   • CData sharing
   • DMaterialized views
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