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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet:

A) The code will execute without error, and Snowflake's query optimizer will likely combine the two 'filter' operations into a single scan of the table.
B) The code will throw a NullPointerException as null values are not allowed in Snowpark DataFrames.
C) The code will result in a compilation error because 'df2 is not explicitly materialized before being used.
D) Two separate scans of the 'my_table' table will always be performed, one for each 'filter' operation.
E) The final 'DataFrame' 'df3' will only contain rows where 'coll' is greater than 10 and 'c012 is not null.

2. You are tasked with creating a Snowpark DataFrame from a complex JSON structure stored in a VARIANT column named 'payload' within a table called 'events'. The 'payload' contains nested objects and arrays, and you need to extract specific fields into separate columns of the DataFrame. You need to extract the 'event_id' (INT) from the top level of the JSON, the 'user _ id' (INT) from the 'user' object nested within the 'payload' , and the first element of the 'tags' array (VARCHAR) also nested within the 'payload'. Which of the following code snippets correctly defines the schema using 'StructType' and 'StructField' and applies it during DataFrame creation assuming events table contains multiple rows?

A)

B)

C)

D)

E)

3. You have a CSV file stored in a Snowflake stage named 'my_stage/data.csv'. The file contains customer data, including 'customer id' (INT), 'first_name' (VARCHAR), 'last_name' (VARCHAR), and 'email' (VARCHAR). You want to create a Snowpark DataFrame representing this data, explicitly defining the schema for improved type safety and performance. Which of the following code snippets is the MOST efficient and correct way to create the DataFrame with the specified schema, assuming you have a valid Snowpark session object named 'session'?

A)

B)

C)

D)

E)

4. 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 fail because the 'yield' statement is being called after using 'return' in the processing block. Remove the yield statement as it is incompatible.
B) 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.
C) 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.
D) The UDTF will execute correctly and efficiently in Snowpark, correctly processing each row of the CSV and returning the extracted fields as a table.
E) 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.

5. You have a Snowflake table 'user_profiles' with a VARIANT column 'profile_data'. This column contains JSON objects, and one of the fields within these objects is an array called 'interests'. The 'interests' array contains JSON objects, each with 'name' and 'category' fields. You need to use Snowpark to flatten the 'interests' array and extract the 'name' and 'category' for all user profiles, but only for profiles where the user's 'status' is 'active'. You want to write this in the most efficient way possible. Which of the following code snippets will achieve this?

A)

B)

C)

D)

E)

Solutions: