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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark application that needs to connect to Snowflake using programmatic access. You want to use a secure method of authentication. Which of the following methods, when passed as parameters to the 'snowpark.Session.builder.configS method, would be MOST secure and appropriate for production environments?

A) Setting the 'authenticator' parameter to 'snowflake' and rely on default Snowflake authentication mechanism assuming it setup correctly
B) Using 'oauth_access_token' obtained from an external OAuth server.
C) Passing the 'user', 'password', and 'account' parameters directly as strings.
D) Passing the 'user' and 'password' directly, but retrieving the 'account' from an environment variable.
E) Using 'private_key' stored securely and referencing it using 'private_key_file'.

2. Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

A) Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
B) The 'filter' operation Ccol('column_a') > 100') will be executed twice.
C) The 'countl' operation will trigger the materialization and caching of 'filtered_df.
D) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.
E) The 'count? operation will use the cached results of and apply an additional filter on the cached data.

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

4. Consider a scenario where you're developing a Snowpark stored procedure that accesses sensitive data'. Which of the following strategies, when used together, provide a comprehensive approach to secure this stored procedure and protect the underlying data?
Select all that apply:

A) Implementing row-level security policies on the sensitive data tables.
B) Masking sensitive data within the stored procedure using Snowflake's dynamic data masking policies.
C) Using 'EXECUTE AS OWNER and granting the 'SELECT privilege on the sensitive data tables to the stored procedure's owner role.
D) Using 'EXECUTE AS CALLER and relying on the caller's privileges to access the data.
E) Encrypting the stored procedure's code using AES encryption before deployment.

5. You have written a Snowpark Python function that performs a complex calculation involving user-defined functions (UDFs). When running this function on a large dataset, you encounter a 'PicklingError: Can't pickle ': it's not the same object as main.my function'. What is the MOST likely cause of this error, and how can you resolve it?

A) The Snowpark session is not properly initialized. Ensure that the connection parameters are correct.
B) The UDF contains unsupported Python libraries. Ensure that all dependencies are available on the Snowflake worker nodes.
C) The UDF's return type is not correctly specified. Use 'udf(func, to explicitly define the return type.
D) The dataset is too large to be processed in memory. Use 'df.cache()' to persist the intermediate results to disk.
E) The UDF is defined within a local scope or closure, and Snowpark cannot serialize it. Move the UDF definition to the global scope or use 'cloudpickle' explicitly.

Solutions: