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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) Passing the 'user' and 'password' directly, but retrieving the 'account' from an environment variable.
C) Passing the 'user', 'password', and 'account' parameters directly as strings.
D) Using 'private_key' stored securely and referencing it using 'private_key_file'.
E) Using 'oauth_access_token' obtained from an external OAuth server.

2. A Snowpark application is configured to connect to Snowflake using environment variables for authentication. However, the application frequently encounters intermittent connection errors. You suspect that the environment variables are not being correctly accessed by the Snowpark session builder. Which of the following actions could help diagnose and resolve the issue? (Select TWO)

A) Explicitly print the values of the relevant environment variables (e.g., 'SNOWFLAKE_USER , 'SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) within the Snowpark application before attempting to create the session.
B) Check that the environment variables are set at the correct scope (e.g., system-level, user-level, or within the application's execution environment) and are accessible to the Python process running the Snowpark application.
C) Use to explicitly set connection parameters instead of relying on environment variables for debugging.
D) Replace 'session.builder.getorcreate()' with 'Session.builder.appName('MySnowparkApp').create()'
E) Verify that the user account specified in the environment variables has the necessary permissions (e.g., USAGE on the warehouse, database, and schema) in Snowflake.

3. You are developing a Snowpark Python application that processes streaming data using a dynamic table. The application is experiencing frequent 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: Unsupported feature 'Streaming Dynamic Table'. ' errors, even though dynamic tables are enabled in your Snowflake account and the user has the necessary privileges. Which of the following are potential causes and solutions for this error? (Select TWO)

A) The dynamic table definition contains unsupported SQL syntax, such as 'QUALIFY with complex window functions. Rewrite the dynamic table definition to use standard SQL constructs.
B) The dynamic table materialization schedule is too frequent, overwhelming the Snowflake warehouse. Increase the 'WAREHOUSE_SIZE' parameter of the dynamic table definition.
C) The Snowpark Python client version is outdated and does not support streaming dynamic tables. Upgrade to the latest version of the 'snowflake-snowpark- python' package.
D) The warehouse being used for the Snowpark session is not configured with the feature enabled. Verify that the warehouse configuration includes = TRUE'.
E) The user role lacks the 'EXECUTE MANAGED TASK privilege. Grant this privilege to the user role executing the Snowpark application.

4. You are working with semi-structured data in Snowflake stored in a VARIANT column named 'payload'. You want to extract specific fields from this VARIANT column within a SQL query used to create a Snowpark DataFrame. Which of the following approaches allows you to access nested fields within the 'payload' column directly in the SQL query and create a corresponding column in your Snowpark DataFrame? Select all that apply.

A) Extract the VARIANT data into a Pandas DataFrame and then use Pandas to access the nested fields before creating the Snowpark DataFrame.
B) Use the 'payload:fieldl :field2 syntax directly within the SELECT statement in the SQL query.
C) Use the "LATERAL FLATTEN(input payload)' function within the SQL query to unnest the VARIANT and then access the fields.
D) First create a temporary table containing only the extracted fields using a separate SQL query, then create a Snowpark DataFrame from that table.
E) Use the 'fieldl .field2')' function within the SELECT statement in the SQL query.

5. You have a Snowpark DataFrame 'employees df representing employee data'. You need to update the 'salary' column for employees in the 'Sales' department by applying a 10% increase. Which of the following Snowpark code snippets correctly performs this update? Assume a Snowflake table named 'employees' exists and 'employees df' is correctly created from it.