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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?

A)

B)

C)

D)

E)

2. You have a Snowflake table named 'raw events' with a VARIANT column named 'event data'. The 'event data' column contains JSON objects with a field 'timestamp' that is sometimes represented as a string and sometimes as a number (Unix epoch). You need to create a Snowpark DataFrame that extracts the 'timestamp' as a timestamp object, handling both string and numeric representations. Which of the following code snippets correctly accomplishes this, avoiding errors when encountering incompatible types?

A)

B)

C)

D)

E)

3. You have a Snowpark Python stored procedure that needs to access environment variables stored securely within Snowflake. Which of the following code snippets demonstrates the correct way to retrieve the value of an environment variable named 'API KEY' within your stored procedure?

A)

B)

C)

D)

E)

4. You have a Snowpark DataFrame named with the following schema: 'product_id' (INTEGER), (STRING), 'category' (STRING), 'price' (FLOAT), and 'description' (STRING). You want to perform several data cleaning and transformation steps. Which of the following operations can be efficiently chained together using Snowpark DataFrames to clean null values in 'description', replace special characters in 'product_name' and standardize 'category' values? Select all that apply:

A) Using the function to remove special characters (e.g., '$', '#, '@') from the 'product_name' column using a regular expression.
B) Using the method to replace null values in the 'description' column with a default string 'No description available'.
C) Using a UDF (User-Defined Function) written in Python to standardize the 'category' column by converting all values to lowercase and removing leading/trailing spaces.
D) Manually iterating through each row of the DataFrame and applying Python string manipulation functions to clean the data. (e.g. row['description'] =
E) Using the 'coalesce' function to fill null values in 'description' with values from a separate 'backup_description' column (if available).

5. You have a Snowpark DataFrame named with columns 'category', , and You want to perform the following transformations using Snowpark:

A)

B)

C)

D)

E)

Solutions: