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最新的 Snowflake Certification SPS-C01 免費考試真題:

1. You have two Snowpark DataFrames: 'employees_df with columns 'employee_id' (INTEGER), 'employee_name' (STRING), 'department_id' (INTEGER), and 'salaries_df' with columns 'employee_id' (INTEGER), "salary' (FLOAT), 'effective_date' (DATE). You need to create a new DataFrame that contains the employee's name, department, and the highest salary they have ever received. Assuming there can be multiple salary entries for the same employee with different 'effective date' values, which of the following Snowpark code snippets would correctly and efficiently solve this problem?

A)

B)

C)

D)

E)

2. You are tasked with processing a Snowpark DataFrame named 'orders df that contains order information. The DataFrame includes the following columns: 'order _ id' (INTEGER), 'customer_id' (INTEGER), 'order_date' (DATE), 'order_total' (STRING), and 'discount_code' (STRING). The 'order_total' column contains values with leading dollar signs and commas (e.g., '$1 ,234.56'). The column can contain codes like 'SAVEIO', 'SAVE20', or be NULL. Your goal is to create a new DataFrame 'transformed_df that includes the following transformations: 1 . Convert the 'order_total' column to a numeric value (DOUBLE) after removing the dollar signs and commas. 2. Apply a discount based on the 'discount_code'. If the 'discount_code' is 'SAVEIO', apply a 10% discount; if it's 'SAVE20', apply a 20% discount. If the 'discount_code' is NULL or any other value, apply no discount (0%). 3. Calculate the 'final_total' after applying the discount. Which of the following code snippets correctly and efficiently implements these transformations using Snowpark?

A)

B)

C)

D)

E)

3. You are tasked with deploying a Snowpark Python application that utilizes a third-party library, 'scikit-learn' , for machine learning tasks. The application will be executed as a Snowflake Stored Procedure. What are the necessary steps to ensure the 'scikit-learn' library is available within the Snowpark environment?

A) Include the 'scikit-learn' library directly in the Snowpark session using 'session.add_import(sklearny.
B) Upload the 'scikit-learrf library as a ZIP file to a Snowflake stage, create a Python UDF that unzips the library, and then import the library within the Snowpark Stored Procedure.
C) Create a Snowflake Anaconda environment using conda, include the 'scikit-learns package in the environment, and then create a Snowpark Stored Procedure that utilizes the environment via the 'packages' parameter in the CREATE PROCEDURE statement.
D) Create a Snowflake Anaconda channel integration, add the 'scikit-learn' package to the channel, and then reference the channel in the Snowpark session configuration.
E) Install scikit-learn on your local machine, package your snowpark code into a zip file and upload it to a stage, no extra steps are required.

4. You have JSON files stored in an internal stage named 'json_stage' within your Snowflake account. Each JSON file contains an array of product objects, with potentially nested structures. You need to create a Snowpark DataFrame to analyze this data, but the schema is complex and you want to avoid explicitly defining it in your Python code. Which of the following Snowpark code snippets will MOST effectively achieve this, assuming you have a Snowpark session object named 'session'?

A)

B)

C)

D)

E)

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 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'))
B) ...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'))
C) ...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'))
D) ...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'))
E) ...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'))

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