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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?

A) GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;
B) GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
C) GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;
D) GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,
E) GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;

2. You are tasked with creating a Snowpark UDTF (User-Defined Table Function) in Python to process a large CSV file stored in a Snowflake stage. Each row in the CSV represents a transaction, and you need to parse each row and extract specific fields based on a complex set of rules. The UDTF should return a table with the extracted fields. Consider the following code snippet:

A) The UDTF will run but will not return any data since the code currently lacks a 'session' object properly initialized for Snowpark operations inside the handler. Ensure the handler method has the session parameter and uses it.
B) The UDTF will run, but it will be slow due to the use of pandas DataFrame operations within the UDTF. Consider optimizing the code to use Snowpark DataFrame operations instead.
C) The UDTF will execute correctly and efficiently in Snowpark, correctly processing each row of the CSV and returning the extracted fields as a table.
D) The code will raise an error because the 'read_csvs function is not available within the Snowpark UDTF context. The input needs to be processed differently.
E) The UDTF will fail because the 'yield' statement is being called after using 'return' in the processing block. Remove the yield statement as it is incompatible.

3. You are working with two large Snowpark DataFrames: 'transaction_df and 'product df. 'transaction_df contains transaction data including 'transaction id', 'product id', and 'transaction_date'. 'product df contains product details including 'product id', product_name', and 'product category'. You need to join these DataFrames to analyze transaction data by product category. The 'transaction_df is significantly larger than 'product_df. Which of the following strategies can significantly improve the performance of the join operation in Snowpark? (Select all that apply)

A) Cache the 'transaction_df DataFrame before the join operation using
B) Use a broadcast join by explicitly specifying 'broadcast-True in the 'join' function when joining 'product_df to 'transaction_df.
C) Use a 'hint' to force Snowflake to use a specific join algorithm like 'MERGE JOINS.
D) Filter the 'transaction_df to a smaller subset based on 'transaction_date' before performing the join, if only recent transactions are needed.
E) Ensure that the 'product_id' column in both DataFrames is of the same data type and has statistics collected on it.

4. You have a Snowpark DataFrame with columns 'department' , and 'salary'. You want to identify employees in each department whose salary is within the top 20% of salaries for that department. Which of the following approaches, using window functions, is the MOST efficient way to achieve this?

A) Calculate the maximum salary per department, then filter employees whose salary is greater than or equal to 80% of the maximum salary.
B) Use the window function to calculate the percentile rank of each employee's salary within their department, then filter for ranks greater than or equal to 0.8.
C) Use window function to rank employees within each department by salary, then calculate the 80th percentile salary using a separate aggregation and join back to the original DataFrame to filter.
D) Use the 'ntile(5)' window function to divide each department's employees into 5 buckets based on salary, then select employees in the top bucket.
E) Calculate the average salary per department, then filter employees whose salary is greater than 80% of the average salary.

5. You are tasked with optimizing a Snowpark Python stored procedure that performs complex data transformations on a DataFrame. The procedure frequently encounters out-of-memory errors when processing large datasets. Which of the following strategies could you implement to mitigate these memory issues within the stored procedure's code ? Choose all that apply.

A) Utilize the 'repartition()' or functions to control the number of partitions in the DataFrame and potentially reduce memory consumption per partition.
B) Use smaller data types (e.g., ' Int16' instead of ' Int64') where appropriate to minimize memory footprint.
C) Implement data filtering and aggregation as early as possible in the transformation pipeline to reduce the size of the DataFrame.
D) Leverage the 'sample()' function to work with a smaller subset of the data for testing and debugging.
E) Increase the warehouse size to provide more memory resources.

Solutions: