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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame named 'employees_df' that contains employee data, including a column named 'hire_date' of type String. The date format in the 'hire_date' column is 'MM/DD/YYYY'. You need to convert this column to a DateType so you can perform date-based calculations. Which of the following Snowpark code snippets correctly converts the column to a DateType?

A)

B)

C)

D)

E)

2. You have a Snowflake table 'PRODUCT CATALOG' with columns 'PRODUCT ID, 'PRODUCT NAME, and 'CATEGORY ID. You also have a table 'CATEGORY' with 'CATEGORY ID' and 'CATEGORY NAME. You need to create a Snowpark DataFrame that joins these two tables and includes only 'PRODUCT NAME and 'CATEGORY NAME. Assume a Snowpark Session object named 'session' is available. Which code snippet demonstrates creating the DataFrame using Snowpark's join functionality and column selection while using the 'table' method?

A)

B)

C)

D)

E)

3. You have two Snowflake tables, 'customers' and 'orders'. The 'customers' table contains customer information, including a 'customer id' and 'region'. The 'orders' table contains order information, including 'order id', 'customer id', and 'order amount'. You need to create a Snowpark DataFrame that joins these two tables on 'customer id' and calculates the total order amount per region. However, some customers may not have any orders, and you want to include all customers in the result, with a total order amount of 0 for those without orders. Which of the following Snowpark code snippets will achieve this goal MOST efficiently, assuming 'customers_df and 'orders_ff are pre-existing Snowpark DataFrames representing the respective tables?

A)

B)

C)

D)

E)

4. You are working with a Snowpark DataFrame containing employee data, including columns 'employee_id', 'first_name', 'last_name', 'salary', and 'department'. You need to perform the following transformations: 1. Concatenate 'first_name' and into a new column called separating them with a space. 2. Increase each employee's salary by a percentage based on their 'department'. Department 'Sales' gets a 10% raise, 'Marketing' gets a 15% raise, and all other departments get a 5% raise. 3. Create a new column reflecting this raise. Which of the following Snowpark code snippets achieves these transformations correctly and efficiently? (Select all that apply)

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date', and 'total amount'. You need to create a new DataFrame that contains only the 'customer_id" and the total number of orders placed by each customer. However, you want to perform this aggregation in parallel using a user-defined function (UDF) to improve performance. Which approach is MOST efficient and CORRECT?

A) Create a UDF that takes an iterator of order IDs and returns the count. Apply this UDF to each partition of the DataFrame using 'maplnPartitions' .
B) Create a UDF that performs the entire aggregation and call it with 'orders_df. The UDF uses a Pandas DataFrame internally to perform the count.
C) Use 'groupBy' to group by 'customer_id' and then use the 'count' aggregate function. Do not use a UDF.
D) Create a UDF that calculates the mode of total_amount, then apply the UDF to the DataFrame using 'select'.
E) Create a UDF that takes a customer ID as input and returns the count of orders for that customer, then apply this UDF to each distinct customer ID using 'map'.

Solutions: