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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineer is tasked with creating a Snowpark Python application that needs to access data from multiple Snowflake accounts and regions. All accounts are using Snowflake's Business Critical edition. Which of the following approaches would be the MOST efficient and maintainable for managing and switching between different Snowpark sessions in this scenario?

A) Create a configuration file (e.g., YAML or JSON) that stores the account identifiers and other connection details for each Snowflake account and region. Load this configuration and create separate Snowpark session objects for each account, storing them in a dictionary or list.
B) Create separate Python scripts for each account and region, hardcoding the account identifiers and credentials within each script.
C) Use the 'snowflake.connector.connect' method to directly establish connections without using the Snowpark Session object.
D) Use a single Snowpark session object and dynamically update the connection parameters (account identifier, username, password) whenever switching to a different account and region.
E) Create a new Anaconda environment for each Snowflake account and install the necessary packages in each environment.

2. You are working with a Snowpark DataFrame 'products df' containing product information, including 'product_id', 'price', and 'discount'. You need to update the 'price' column in the 'products' table based on the following logic: If 'discount' is greater than 0.2, reduce the 'price' by 15%. If 'discount' is between 0.1 and 0.2 (inclusive), reduce the 'price' by 5%. Otherwise, keep the 'price' as is. Which of the following Snowpark code snippets efficiently implements this update? Assume 'products' table already exists and is correctly populated.

A) Option B
B) Option D
C) Option E
D) Option C
E) Option A

3. A data engineering team has created several Snowpark Python UDFs and UDTFs in the 'TRANSFORMATIONS' schema of the 'ANALYTICS' database. A data science team needs to use these functions in their data analysis notebooks. What is the MINIMUM set of privileges that must be granted to the data science team's role ('DATA SCIENTIST') to allow them to discover and execute these UDFs and UDTFs?

A) GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
B) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
C) GRANT ALL PRIVILEGES ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON SCHEMA ANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
D) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
E) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST,

4. You have a Snowpark DataFrame 'df containing customer data with columns 'customer_id', 'signup_date' (TIMESTAMP NTZ), and 'country'. You need to create a new DataFrame that calculates the number of days since each customer signed up, but only for customers in 'USA' and 'Canada'. Furthermore, you want to filter out records where the signup was more than 365 days ago. Which of the following Snowpark code snippets will achieve this most efficiently?

A)

B)

C)

D)

E)

5. 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)

Solutions: