Transform Data Using SQL and Snowpark DEA-C02 Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A data engineer is writing a Snowpark Python transformation that filters out rows where the value in a column is NULL. Which Snowpark DataFrame method should they use?

   • Adf.dropna()
   • Bdf.filter(df.col('column').is_not_null())
   • Cdf.where(df.col('column') == None)
   • Ddf.select(df.col('column'))
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2. 7expert · hard

   A data engineer needs to create a Python UDF that processes a large number of rows and performs a complex calculation. The UDF will be used in a SQL query on a large table. Which UDF type should the engineer choose to maximize performance?

   • AScalar UDF
   • BVectorized UDF
   • CTabular UDF
   • DSQL UDF
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4. 8application · medium

   A company needs to transform streaming data from IoT devices as it arrives, enriching it with reference data and writing it to a Snowflake table for near-real-time analytics. Which Snowflake feature should they use to implement this transformation?

   • AUse Snowpipe to load data and then run a transformation query.
   • BCreate a stream on the raw table and a task that runs a SQL transformation on the stream.
   • CUse a materialized view to automatically transform the data.
   • DUse Snowpark streaming to process data in micro-batches and write to a table.
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5. 9expert · hard

   A data engineer is optimizing a Snowpark job that performs multiple aggregations on a large table. The job is slow because of excessive shuffling. Which optimization should the engineer apply to reduce shuffling?

   • AUse df.collect() to bring data to the client and aggregate locally.
   • BUse df.groupBy() with multiple aggregations in a single call.
   • CUse df.cache() to avoid recomputing the DataFrame.
   • DUse df.repartition() to increase the number of partitions.
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6. 10application · medium

   A data engineer needs to apply a complex string-cleaning function to a column in a large table. The function is written in Python and will be used in many SQL queries. Which approach should the engineer use to make this function available for SQL queries?

   • ACreate a view that applies the cleaning logic using string functions.
   • BCreate a Python UDF and register it with CREATE FUNCTION.
   • CCreate a SQL UDF that calls the Python function via a stored procedure.
   • DCreate a Snowpark DataFrame transformation and use it in every query.
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