Snowflake Certified SnowPro Specialty - Snowpark - SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You're developing a Snowpark application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a different table. You want to ensure that the entire process is executed as a single atomic transaction, even if it involves multiple Snowpark DataFrames and operations. Which of the following actions are required to achieve this transactional behavior?

A) Ensure that the target table for writing the results has the 'TRANSIENT' property set to 'TRUE'.
B) Configure the Snowpark session with the parameter set to ' FALSE
C) Leverage the 'CREATE OR REPLACE TABLE AS SELECT statement within a Stored Procedure called from your Snowpark code. All DML operations done as part of stored proc is transactional
D) Explicitly start a transaction using 'session.beginTransaction()' at the beginning of the Snowpark application and commit it using 'session.commitTransaction(Y at the end.
E) All Snowpark operations within a single session are automatically executed as a single atomic transaction by default; no additional configuration is required.

2. You have a Snowpark DataFrame 'df with a column 'transaction_date' of STRING type, containing dates in 'YYYY-MM-DD' format, and a 'transaction_amount' column of STRING type, containing currency values like '$1 ,234.56'. You need to create a new DataFrame 'df_transformed' that contains the 'transaction_date' as a DATE type and 'transaction_amount' as a DOUBLE type. What Snowpark Python code snippet will accomplish this transformation most efficiently and handle potential errors during the cast?

A) ...python from snowflake.snowpark.functions import try_to_date, try_to_double df_transformed = df.with_column('transaction_date', try_to_date(col('transaction_date'))).with_column('transaction_amount', try_to_double(regexp_replace(col('transaction_amount'), '[$,]', ")))
B) ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date'))).with_column('transaction_amount', to_double(col('transaction_amount').replace('$',").replace(',',")))
C) ...python import snowflake.snowpark.functions as sf df_transformed = df.with_column('transaction_date', sf.to_date(sf.col('transaction_date'), format='YYYY- MM-DD')).with_column('transaction_amount', sf.to_double(sf.regexp_replace(sf.col('transaction_amount'), '[$,]', ")
D) ...python df_transformed = df.select(to_date(col('transaction_date')).alias('transaction_date'), to_double(col('transaction_amount')).alias('transaction_amount'))
E) ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date') YYYY-MM-DD')).with_column('transaction_amount', col('transaction_amount').cast(DoubleType()))

3. You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?

A)

B)

C)

D)

E)

4. You are tasked with optimizing a Snowpark Python application that performs complex data transformations using a large DataFrame. The application is running slower than expected. You suspect that data skew is causing uneven distribution of work across the Snowflake warehouse nodes. Which of the following techniques could be used to mitigate data skew and improve the performance of your Snowpark application? (Select TWO)

A) Utilize Snowflake's automatic clustering feature on the underlying table to improve data locality.
B) Increase the warehouse size to the largest possible option.
C) Use the function to redistribute the data evenly across the warehouse nodes based on a specific column or set of columns.
D) Use the function with the 'BROADCAST' strategy for smaller DataFrames that are joined with the large DataFrame.
E) Use the function to sort the data before performing the transformations.

5. You have a Snowpark DataFrame containing customer transaction data'. Your goal is to save this DataFrame as a set of Parquet files in an existing Snowflake stage named , partitioned by the 'transaction_date' column. You want to ensure that the files are automatically compressed using the Zstandard codec and that existing files with the same name are overwritten. Which of the following Snowpark code snippet achieves this with the most optimal approach and respects best practices?

A) Option C
B) Option B
C) Option A
D) Option D
E) Option E

Solutions: