Try and practice the latest Snowflake : SPS-C01 real questions & answers

You are developing a Snowpark application to process large datasets stored in Snowflake. You need to create a session using the 'snowflake.connector.connect' method. Which of the following code snippets correctly establishes a session with Snowflake, leveraging an external browser authentication mechanism, ensuring secure and reliable access to your data while minimizing exposed credentials in the code?

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You are tasked with creating a Snowpark stored procedure to perform complex data transformations using a Pandas DataFrame. You want to optimize the performance of the stored procedure by leveraging Snowpark's distributed execution capabilities. Consider the following code snippet:

Which of the following changes to the above code will significantly improve the performance by utilizing Snowpark's distributed execution?

• A. Before calling , apply a 'limit()' function to the Snowflake Dataframe to reduce the size of the Pandas DataFrame to only a subset of the data. Then, apply the complex transformation.
• B. Replace with 'session.table('my_table')'. Perform the 'some_complex_transformation' directly using Snowpark DataFrame operations instead of converting to Pandas.
• C. Keep the code as is. Pandas DataFrames are always automatically distributed within Snowpark.
• D. Split the Pandas DataFrame into smaller chunks and process each chunk in parallel using Python's multiprocessing library before converting back to a Snowpark DataFrame. Then save it to a table.
• E. Use instead of 'to_pandas()'. Then, after the transformation, use 'session.write_pandas(transformed_df, 'transformed_table')'.

You have developed a Snowpark Python stored procedure that calculates the average sales per region from a large sales data table. The procedure is currently defined inline within your Snowflake notebook. You want to operationalize this by creating the stored procedure from a local Python file named The file contains the following code: "'python from snowflake.snowpark.session import Session def calculate_avg_sales(session: Session, sales_table_name: str, region_column: str, sales_column: str) -> float: sales df = session.table(sales table name) avg_sales df = sales_df.group_by(region_column).agg({sales_column: 'avg'}) avg_sales = avg_sales_df.collect() return avg_sales[0][1] Which of the following code snippets correctly creates the stored procedure 'AVG SALES PROC in Snowflake, referencing the Python file, and handles potential dependency issues? Assume you have already established a Snowpark session named 'session' and that the stage 'my_stage' already exists.

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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?

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You have a Snowpark DataFrame 'product_df and you want to create a view named 'active_products' that only includes products with a 'status' column value equal to 'ACTIVE'. This view should be accessible to all users in the 'PUBLIC' role. Which of the following code snippets correctly creates the view and grants the necessary privileges?

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