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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題:

1. You have a Snowpark DataFrame named with the following schema: '(timestamp: TmestampType, sensor_id: StringType, value: FloatType)'. You need to identify the top 3 sensors with the highest average value over the entire dataset. Which of the following Snowpark Python code snippets correctly implements this requirement?

A)

B)

C)

D)

E)

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

A)

B)

C)

D)

E)

3. You have a Snowpark Python stored procedure that performs complex data transformations. This stored procedure needs to read data from a large table ('TRANSACTIONS) and write the transformed data to another table PROCESSED TRANSACTIONS'). You want to optimize the performance of this stored procedure by leveraging Snowpark's features for parallel processing. Which of the following approaches can significantly improve the performance of the stored procedure, assuming sufficient warehouse resources are available?

A) Use Snowflake's standard SQL queries within the stored procedure to read and transform the data. Write the results to the 'PROCESSED TRANSACTIONS table using 'INSERT statements.
B) Read the entire 'TRANSACTIONS table into a Pandas DataFrame within the stored procedure and perform the transformations using Pandas functions. Then, write the transformed data back to the table using Snowpark's 'createDataFrame' and 'write' methods.
C) Load the data from 'TRANSACTIONS' table into a temporary table within the stored procedure, then use standard SQL queries on the temporary table for transformations, finally using snowpark DataFrame API to write it back to the 'PROCESSED_TRANSACTIONS' table.
D) Use Snowpark's 'sprocs decorator with appropriate 'packages' and leverage the Snowpark DataFrame API with vectorized UDFs to transform the data. Use 'session.write_pandaS to write the Pandas DataFrame to the 'PROCESSED_TRANSACTIONS' table after the transformation.
E) Use the Snowpark DataFrame API to read the 'TRANSACTIONS' table and apply transformations using vectorized UDFs. Then, use the 'write' method to write the transformed data to the 'PROCESSED TRANSACTIONS' table.

4. A data engineering team wants to deploy a Snowpark Python stored procedure that aggregates sales data from a table 'SALES DATA and writes the results to a table 'AGGREGATED SALES. The stored procedure needs to be executed by various users with different roles. The team wants to ensure that users can only execute the stored procedure and cannot directly access the underlying 'SALES DATA' table. Which approach is most suitable for managing data access and security in this scenario, and what are the implications of using 'EXECUTE AS OWNER vs 'EXECUTE AS CALLER?

A) Create a view on the 'SALES_DATX table that only exposes the necessary columns and grant 'SELECT privilege on the view to the roles that need to execute the stored procedure. Create the stored procedure with EXECUTE AS CALLER to leverage the view's column restrictions.
B) Create the stored procedure with 'EXECUTE AS OWNER , grant 'USAGE on the database and schema. Grant 'EXECUTE TASK' on the stored procedure to the specific roles, while the Owner(Role with Execute Task permission) should have access to SALES DATA table.
C) Create the stored procedure with 'EXECUTE AS CALLER and grant 'SELECT privilege on the 'SALES DATA' table to all roles that need to execute the stored procedure. This allows the stored procedure to execute with the caller's privileges, and the caller must have the necessary privileges to access the underlying tables.
D) Create the stored procedure with 'EXECUTE AS CALLER and grant 'USAGE on the database and schema. The callers must have access to both the AGGREGATED SALES and SALES DATA tables. The stored procedure will use the caller's privileges for all operations.
E) Create the stored procedure with 'EXECUTE AS OWNER and grant 'USAGE privilege on the database and schema containing the stored procedure to the roles that need to execute it. This hides the underlying table from the caller, and the stored procedure executes with the owner's privileges.

5. You have a Python function, 'calculate metrics(df: snowpark.DataFrame, metric name: str) -> snowpark.DataFrame', that calculates various metrics on a Snowpark DataFrame. You want to deploy this function as a stored procedure in Snowflake. You need to ensure that the stored procedure has appropriate permissions to read data from a table named 'customer data' and write results to a table named 'metrics_table'. Which of the following steps are necessary to achieve this, assuming you are using the 'session.sproc.register' method?

A) Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run 'calculate_metrics' function.
B) Grant the 'SELECT privilege on the 'customer_data' table and the 'INSERT privilege on the 'metrics_table' table to the role executing the stored procedure.
C) When registering the stored procedure using 'session.sproc.register' , specify the argument and provide a 'replace=True' if necessary. This will allow you to assign ownership of the stored procedure to a role with the necessary privileges.
D) Specify the 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.
E) Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.

質問と回答：