Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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Snowflake SPS-C01 考試大綱主題:

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

1. You are using Snowpark Python to analyze sales data stored in a Snowflake table named 'SALES DATA. The table has columns PRODUCT ICY, 'REGION', and 'SALE DATE. You need to calculate the total sale amount for each product in each region. You intend to use the 'group_by' and 'agg' functions. Which of the following Snowpark Python code snippets correctly performs this aggregation and renames the aggregated column to 'TOTAL SALES'? (Assume 'session' is a valid Snowpark session object.)

A)

B)

C)

D)

E)

2. You have created a Snowpark stored procedure in Python that accesses a Snowflake stage to read configuration files. To enhance security, you want to grant the stored procedure specific permissions to only read files from that stage, without granting broader account- level access. Which of the following approaches is the MOST secure and granular way to achieve this?

A) Create a stored procedure with 'EXECUTE AS CALLER and ensure the caller (user executing the procedure) has the necessary privileges.
B) Create a Snowflake custom role and grant the 'READ' privilege on the stage to this role, then grant this role to the stored procedure's owner.
C) Grant the 'READ privilege directly to the stored procedure using the 'ALTER PROCEDURE command.
D) Grant the 'USAGE privilege on the stage to the role that owns the stored procedure.
E) Grant the 'SELECT privilege on the stage to the role that owns the stored procedure.

3. 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 'coalesce' function to fill null values in 'description' with values from a separate 'backup_description' column (if available).
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 function to remove special characters (e.g., '$', '#, '@') from the 'product_name' column using a regular expression.

4. You are using Snowpark in Python within a Jupyter Notebook environment to analyze sales data'. You've established a connection to Snowflake and loaded your data into a Snowpark DataFrame named 'sales ff. You need to calculate the cumulative sales for each product category over time. The 'sales_df' DataFrame has columns 'SALE DATE' (DATE), 'PRODUCT CATEGORY' (VARCHAR), and 'SALE AMOUNT' (NUMBER). Which of the following approaches, or combination of approaches, will correctly calculate the cumulative sales while optimizing for Snowflake's performance and scalability? (Select all that apply)

A) Iterate through the rows of the 'sales_df DataFrame in the Jupyter Notebook and manually calculate the cumulative sales using Python code.
B) Use session.sql() to execute a SQL query with a window function for cumulative sales and load the data into snowpark DataFrame.
C) Write a stored procedure in Snowflake that calculates the cumulative sales and call the stored procedure from your Snowpark application.
D) Use a Window function within a Snowpark DataFrame transformation to calculate the cumulative sum for each product category, ordered by 'SALE_DATE.
E) Use the and 'sort()' functions on the Snowpark DataFrame to order data by 'PRODUCT CATEGORY' and 'SALE DATE before calculating the cumulative sum using a UDF.

5. You have developed a Snowpark Python application that needs to connect to an external REST API to enrich data during a transformation. The API requires authentication using an API key stored securely. Which of the following approaches is the MOST secure and recommended way to manage the API key within the Snowpark environment?

A) Encrypt the API key using a third-party encryption library and store it in a Snowflake table.
B) Store the API key as an environment variable within the Snowflake session.
C) Hardcode the API key directly into the Snowpark Python code.
D) Store the API key in a Snowflake Secret Object and retrieve it within the Snowpark Python code using the function.
E) Store the API key in a secure vault outside of Snowflake and retrieve it using a custom Snowflake external function.

問題與答案：