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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python application that processes streaming data using a dynamic table. The application is experiencing frequent 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: Unsupported feature 'Streaming Dynamic Table'. ' errors, even though dynamic tables are enabled in your Snowflake account and the user has the necessary privileges. Which of the following are potential causes and solutions for this error? (Select TWO)

A) The dynamic table definition contains unsupported SQL syntax, such as 'QUALIFY with complex window functions. Rewrite the dynamic table definition to use standard SQL constructs.
B) The dynamic table materialization schedule is too frequent, overwhelming the Snowflake warehouse. Increase the 'WAREHOUSE_SIZE' parameter of the dynamic table definition.
C) The Snowpark Python client version is outdated and does not support streaming dynamic tables. Upgrade to the latest version of the 'snowflake-snowpark- python' package.
D) The warehouse being used for the Snowpark session is not configured with the feature enabled. Verify that the warehouse configuration includes = TRUE'.
E) The user role lacks the 'EXECUTE MANAGED TASK privilege. Grant this privilege to the user role executing the Snowpark application.

2. A data engineering team is using Snowpark Python to build a data pipeline. They need to create a User-Defined Function (UDF) that transforms a JSON string column representing customer information into a STRUCT type containing flattened fields for 'name', 'age', and 'city'. The UDF should handle null values gracefully and return NULL if the input JSON is invalid or if the 'name' field is missing. Considering performance implications and error handling, which of the following approaches is MOST optimal for defining and registering this UDF?

A) Using 'snowflake.snowpark.functions.sproc' to create a stored procedure that performs the JSON transformation and returns the transformed data.
B) Using 'snowflake.snowpark.functions.udf with and relying solely on Snowflake's built-in JSON functions within the UDF, even for complex transformations, and handling exceptions with try-except blocks within the UDF to return NULL.
C) Using 'snowflake.snowpark.functions.udf with defining the STRUCT schema explicitly, and handling JSON parsing and field extraction using the 'snowflake.snowpark.functions.parse_json' function. Return None for invalid json.
D) Using 'snowflake.snowpark.functions.udf with and handling JSON parsing and field extraction using standard Python libraries within the UDF, returning a JSON string representation of the STRUCT.
E) Using 'session.register_function' to register a Python function as a UDF with and manually constructing a VARIANT object in Python from the extracted JSON fields.

3. 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)

4. You are working with a Snowpark DataFrame 'df that contains user profile data'. A column named 'profile' stores user information as JSON, including 'age' (which can be a number or a string), 'is active' (which can be a boolean or a string 'true'/'false'), and registration date' (stored as a string in 'YYYY-MM-DD' format). You need to perform the following data transformations: 1. Cast the 'age' to an integer, defaulting to -1 if casting fails. 2. Cast 'is active' to a boolean, treating 'true' (case-insensitive) as true and any other string as false. 3. Convert 'registration_date' to a date object. Select the code snippets (multiple answers can be correct) that correctly accomplish these tasks using Snowpark DataFrame transformations.

A)

B)

C)

D)

E)

5. You are tasked with optimizing a Snowpark Python application that performs complex data transformations using a large DataFrame. The application is running slower than expected. You suspect that data skew is causing uneven distribution of work across the Snowflake warehouse nodes. Which of the following techniques could be used to mitigate data skew and improve the performance of your Snowpark application? (Select TWO)

A) Use the function to redistribute the data evenly across the warehouse nodes based on a specific column or set of columns.
B) Use the function to sort the data before performing the transformations.
C) Use the function with the 'BROADCAST' strategy for smaller DataFrames that are joined with the large DataFrame.
D) Utilize Snowflake's automatic clustering feature on the underlying table to improve data locality.
E) Increase the warehouse size to the largest possible option.

Solutions: