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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineering team is migrating a series of complex SQL queries into Snowpark Python to leverage vectorized UDFs and optimize performance. They currently use several Common Table Expressions (CTEs) within their SQL queries. What is the most efficient and Pythonic approach to create a Snowpark DataFrame representing the result of a complex SQL query with multiple CTEs, minimizing code redundancy and maintaining readability?

A) Create separate temporary tables in Snowflake for each CTE using SQL, then create Snowpark DataFrames from these temporary tables using session.table(table_name)'.
B) Utilize the method to create a single Snowpark DataFrame by executing the entire SQL query with CTEs. Then, use Snowpark's DataFrame API for further transformations if needed.
C) Use the method to create separate Snowpark DataFrames for each CTE and then use Snowpark DataFrame joins to combine them into the final DataFrame.
D) Re-write all CTEs using Snowpark's DataFrame API directly, avoiding the use of 'session.sql()' altogether.
E) Concatenate the SQL statements representing each CTE and the final SELECT statement into a single long string, then use to create the DataFrame.

2. You are building a Snowpark application using the 'snowflake-cli' to manage Snowflake connections. You have configured multiple connection profiles using 'snowflake connection add'. Which of the following Python code snippets demonstrates the most efficient and idiomatic way to create a Snowpark session using a specific connection profile named 'my_profile' defined in your 'snowflake-cli' configuration? Assume snowflake-cli is correctly configured and authenticated.

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame containing sales data with columns 'region' , and 'sales_amount'. You need to calculate the total sales amount for each region and then filter the results to only include regions where the total sales amount is greater than 10000. Which of the following Snowpark code snippets correctly implements this logic?

A)

B)

C)

D)

E)

4. You are developing a Snowpark application that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?

A)

B)

C)

D)

E)

5. You are tasked with processing a Snowpark DataFrame named 'orders df that contains order information. The DataFrame includes the following columns: 'order _ id' (INTEGER), 'customer_id' (INTEGER), 'order_date' (DATE), 'order_total' (STRING), and 'discount_code' (STRING). The 'order_total' column contains values with leading dollar signs and commas (e.g., '$1 ,234.56'). The column can contain codes like 'SAVEIO', 'SAVE20', or be NULL. Your goal is to create a new DataFrame 'transformed_df that includes the following transformations: 1 . Convert the 'order_total' column to a numeric value (DOUBLE) after removing the dollar signs and commas. 2. Apply a discount based on the 'discount_code'. If the 'discount_code' is 'SAVEIO', apply a 10% discount; if it's 'SAVE20', apply a 20% discount. If the 'discount_code' is NULL or any other value, apply no discount (0%). 3. Calculate the 'final_total' after applying the discount. Which of the following code snippets correctly and efficiently implements these transformations using Snowpark?

A)

B)

C)

D)

E)

Solutions: