SPS-C01 무료 덤프문제 온라인 액세스

When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)

• A.Regularly rotate the key pair.
• B.Encrypt the private key at rest.
• C.Store the private key in an environment variable or a secure secret management system (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault).
• D.Store the private key directly in the Snowpark code repository.
• E.Grant broad access to the environment variable containing the private key to all developers.

You are developing a Snowpark Python application that reads a large dataset (1 TB) from a Snowflake table 'TRANSACTIONS and performs complex aggregations. The application is experiencing significant performance issues, with query execution taking several hours. You have already verified that the warehouse size is appropriate and caching is enabled. You suspect the issue might be related to data skew and incorrect partitioning. Which of the following strategies would be MOST effective in identifying and mitigating this performance bottleneck?

• A.Use partition_expression=sf.rand())' to randomly repartition the DataFrame into 100 partitions, regardless of the data distribution in the ' TRANSACTIONS table.
• B.Implement caching using after reading the data from the 'TRANSACTIONS' table and before performing any aggregations.
• C.Analyze the 'TRANSACTIONS' table's data distribution using and histograms on the join keys. Based on the analysis, use with the most skewed column to redistribute the data more evenly. Also, consider using bucketing if appropriate.
• D.Use to force a broadcast join, assuming the aggregated data is small enough to fit in memory. Monitor query profiles to confirm the broadcast occurs.
• E.Increase the Snowflake warehouse size to the largest available option (e.g., X6-Large) to provide more resources for query execution, without analyzing data distribution.

You have a Python function named 'process data' that performs data cleaning and transformation on a Pandas DataFrame. You want to convert this function into a Snowpark Python stored procedure to leverage Snowflake's compute resources. However, the 'process_data' function relies on several external Python libraries (e.g., 'pandas', 'numpy', 'scikit-learn') that are not pre-installed in the Snowflake environment. Which of the following approaches would ensure that these dependencies are available within the Snowpark stored procedure? Choose all that apply

• A.Use session.custom_package to resolve dependencied
• B.Include the necessary 'import' statements for the libraries within the stored procedure's code. Snowflake will automatically resolve and install the dependencies.
• C.Specify the required packages in the 'packages' argument of the '@sproc' decorator or 'session.add_packageS method.
• D.Create a custom Anaconda environment with the required packages and upload it to a Snowflake stage. Then, specify the stage location in the 'imports' argument of the 'CREATE PROCEDURE statement.
• E.Bundle the required libraries into a ZIP file and upload it to a Snowflake stage. Then, add the ZIP file to the 'imports' list during stored procedure creation, ensuring that the library paths are correctly referenced within the Python code.

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

You have a Snowpark DataFrame containing data to be loaded into a Snowflake table You want to use the 'merge' operation to update existing records and insert new records. The has a column (BOOLEAN) that indicates whether a record should be updated (TRUE) or inserted (FALSE) if it doesn't already exist. Which of the following SQL expressions correctly implement this logic within the 'merge' operation's 'WHEN MATCHED' and 'WHEN NOT MATCHED' clauses?

• A.WHEN MATCHED AND = TRUE THEN UPDATE SET columni = staging_df.columnl WHEN NOT MATCHED AND = FALSE THEN INSERT (columni) VALUES (staging_df.columnl)
• B.WHEN MATCHED THEN UPDATE SET columni = staging_df.columnl WHEN NOT MATCHED THEN INSERT (columni) VALUES (staging_df.columnl)
• C.WHEN MATCHED AND THEN UPDATE SET columni = staging_df.columnl WHEN NOT MATCHED AND NOT THEN INSERT (columni) VALUES (staging_df.columnl)
• D.WHEN MATCHED AND = TRUE THEN UPDATE SET target_table.columnl = staging_df.columnl WHEN NOT MATCHED AND = FALSE THEN INSERT (columni) VALUES (staging_df.columnl)
• E.WHEN MATCHED THEN UPDATE SET target_table.columnl = staging_df.columnl WHEN NOT MATCHED THEN INSERT (columni) VALUES (staging_df.columnl )