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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are optimizing a Snowpark Python application that performs complex data transformations on a large dataset. You notice significant performance bottlenecks. Which of the following optimization techniques would be MOST effective in leveraging the Snowpark architecture to improve performance?

A) Manually partitioning the DataFrame into smaller chunks before applying transformations.
B) Using 'session.sql()' whenever possible instead of Snowpark DataFrame operations.
C) Using User-Defined Functions (UDFs) written in Python for all transformations, ensuring they are vectorized where possible, instead of native Snowpark functions.
D) Exploiting lazy evaluation by chaining transformations together and avoiding unnecessary 'collect()' or 'toPandas()' calls.
E) Converting all Snowpark DataFrames to Pandas DataFrames before performing any transformations.

2. Consider the following Snowpark Python code snippet:

A)

B) The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.
C) This code will fail because is not a valid method for Snowpark DataFrames.
D) The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.
E) This code will ovemrite the table if it already exists.

3. Consider a Snowflake table named 'raw events' containing web event data in a VARIANT column called 'event data'. Each 'event_data' JSON object may contain an array of product IDs under the key 'product_ids', and another array of user IDs who interacted with those products under the key 'user_ids'. These arrays are not guaranteed to be the same length, and a missing array should be handled gracefully (return null/empty result for that row rather than throwing error). Your objective is to create a Snowpark DataFrame that flattens both arrays into rows, pairing each product ID with each user ID for the specific event, generating as many rows as possible, and including event timestamp. You can use a zero-based index for the array elements using 'ARRAY CONSTRUCT()' and then using that array for using the EXPLODE function. Which of the following are necessary steps to fulfill this requirement and which are not?

A) Use 'iff()' or 'case when' and and other relevant function to handle exception cases.
B) Use two separate 'withColumn()' transformations followed by two 'explode()' operations to flatten each array independently.
C) Use function to dynamically create an array of indices for the 'explode' function and use and function to handle missing values
D) Convert the 'product_ids' and 'user_ids' arrays to strings using and before applying 'explode'.
E) Use a 'LEFT OUTER JOIN LATERAL FLATTEN' to flatten 'product_ids' and 'user_ids', and then use 'nvl()' or 'coalesce()' to handle cases where one or both arrays are missing.

4. You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame named 'orders_df with columns 'order_id', 'customer_id', 'order_date', and 'order_total'. You need to perform the following data enrichment steps using Snowpark for Python: 1. Calculate the 'year' from the 'order_date' column. 2. Calculate the 'discounted_total' by applying a discount of 10% if the 'order_total' is greater than $100, otherwise, no discount. 3. Create a new column 'customer_tier' based on the total spend per customer for each year. Customers with total spend greater than $1000 are 'Gold', between $500 and $1000 are 'Silver', and below $500 are 'Bronze'. Which of the following code snippets correctly implements these data enrichment steps using Snowpark (Assume the existence of a customer total spend df DataFrame).

A)

B)

C)

D)

E)

Solutions: