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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet designed to read data from a Snowflake table, apply a user-defined function (UDF) for data transformation, and then write the transformed data to another table. The UDF, 'calculate_score' , requires a configuration file ('config.json') to be loaded. Which of the following code snippets demonstrates the CORRECT and MOST efficient way to load and access the "config.json' file within the UDF, ensuring that it's available to all UDF invocations without requiring network access?

A)

B)

C)

D)

E)

2. You have a Snowpark DataFrame containing product information, and you want to persist it into a Snowflake table named PRODUCTS. You need to handle the following scenarios: 1. If the table 'PRODUCTS does not exist, create it. 2. If the table PRODUCTS' exists, append the data from 'df_products' to it. Which of the following methods can achieve this?

A)

B)

C)

D)

E)

3. Consider two Snowpark DataFrames, 'employees' and 'departments' , with the following schemas: 'employees': (employee_id: Integer Type, employee_name: StringType, department_id: Integer Type, salary: IntegerType) 'departments': (department_id: Integer Type, department_name: StringType, location: StringType) You want to find the highest salary within each department, along with the department name and location, and display the results in a Snowpark DataFrame. Which of the following Snowpark Python code snippets correctly achieves this?

A)

B)

C)

D)

E)

4. You are tasked with deploying a set of Python UDFs and UDTFs to a Snowflake environment using Snowpark. These functions rely on several external Python packages and need to be versioned and managed effectively. Which of the following strategies provides the MOST robust and scalable solution for managing dependencies and deploying these functions in a reproducible manner?

A) Manually uploading the required Python packages to a Snowflake stage and specifying them in the 'packages' argument of the '@sf.UDF and 'session.udtf.register' calls. Update the packages on stage every time dependencies are upgraded.
B) Creating a conda environment specification file (environment.yml) that lists all dependencies, storing the environment.yml file in a Snowflake stage. Update the environment.yml when dependencies are upgraded.
C) Creating a 'requirements.txt' file and including all required packages in it. Zipping this file and uploading it to a Snowflake stage, and specifying it in 'imports'.
D) Options A,B and C are all equally viable options
E) Creating a 'requirements.txt' file, using 'conda' to create an environment.yml and use 'snowflake.snowpark.functions.udf and 'session.udtf.register' with the environment.yml. Update the environment.yml manually when dependencies are upgraded.

5. You have a Snowpark DataFrame named 'sales df containing sales data for different products. The DataFrame includes columns product_id' (INTEGER), 'sale_date' (DATE), 'quantity' (INTEGER), and 'price' (FLOAT). You need to calculate the total revenue for each product on a monthly basis and store the result in a new DataFrame named Which of the following Snowpark code snippets will correctly achieve this, while maximizing performance and minimizing data shuffling?

A) ...python from snowflake.snowpark.functions import to_date, date_trunc, sum monthly_revenue_df = sales_df.withColumn('sale_month', date_trunc('MM', sales_df['sale_date'])).groupBy('product_id', 'sale_month').agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
B) ...python from snowflake.snowpark.functions import date_part, sum monthly_revenue_df = sales_df.groupBy('product_id', date_part('month', 'sale_date').alias('sale_month')).agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
C) ...python from snowflake.snowpark.functions import monthname, sum monthly_revenue_df = sales_df.groupBy('product_id', monthname('sale_date').alias('sale_month')).agg(sum(sales_dfl'quantity'] sales_dfl'price']).alias('total_revenue'))
D) ...python from snowflake.snowpark.functions import date_format, sum monthly_revenue_df = sales_df.withColumn('sale_month', date_format(sales_df['sale_date'], 'yyyy-MM')).groupBy('product_id', 'sale_month').agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
E) ...python from snowflake.snowpark.functions import month, sum monthly_revenue_df = sales_df.groupBy('product_id', month('sale_date').alias('sale_month')).agg(sum(sales_df['quantity'l sales_df['price']).alias('total_revenue'))

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