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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have two Snowpark DataFrames, 'dfl' and 'df2', representing customer data'. 'dfl' contains customer IDs and names, while 'df2' contains customer IDs and email addresses. You need to create a new DataFrame that contains all customer IDs, names, and email addresses, including customers present in only one of the DataFrames. Which Snowpark set operation and join type would be most appropriate for achieving this?

A) INTERSECT and LEFT JOIN
B) UNION ALL and INNER JOIN
C) DISTINCT and CROSS JOIN
D) UNION and FULL OUTER JOIN
E) EXCEPT and RIGHT JOIN

2. You are developing a Snowpark application that requires access to a specific Snowflake database and schema'. You want to configure the application to use different databases and schemas depending on the environment (e.g., development, testing, production). Which of the following approaches, used in conjunction, provide the MOST flexible and maintainable solution?

A) Hardcoding the database and schema names directly into the Snowpark application code.
B) Leveraging a configuration file (e.g., YAML or JSON) to store environment-specific settings, including database and schema names, and loading this file when creating the Snowpark session. Also create separate Snowflake users with specific database permissions for development and production environments.
C) Using a single Snowflake user with default database and schema set at the user level.
D) Utilizing environment variables to store the database and schema names, and referencing these variables when creating the Snowpark session. Example: 'session = Session.builder.configs({ 'database': os.environ['SNOWFLAKE DATABASE'], 'schema': os.environ['SNOWFLAKE SCHEMA']
E) Using Snowflake's session parameter hierarchy to define the database and schema at the account level.

3. You have two Snowpark DataFrames: containing customer information and 'orders_df containing order details. You need to merge these DataFrames based on the column to create a unified view. The 'customers_df may contain duplicate values. The contains recent orders. You want to use 'merge' with an 'UPDATE action based on 'customer id' and a 'WHEN NOT MATCHED BY TARGET action to insert new customer records from 'customers df into the 'orders df table that do not exist.

A) The 'merge' operation will succeed, processing each matching record sequentially. If more than one row matches it will process based on order in the dataframe
B) The merge operation cannot handle duplicate keys in the source DataFrame without pre-aggregation.
C) You must use on the source DataFrame ('customers_df) before using 'merge' to ensure only one matching row exists per customer.
D) The 'UPDATE action in 'merge' will update all matching rows in the target table based on the join condition, regardless of duplicates in the source.
E) The 'merge' operation will fail immediately upon encountering the first duplicate key during the update operation. Consider stage the source dataframe and use an appropriate dedup option with merge.

4. 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) 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.
B) Replace with 'session.table('my_table')'. Perform the 'some_complex_transformation' directly using Snowpark DataFrame operations instead of converting to Pandas.
C) 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.
D) Keep the code as is. Pandas DataFrames are always automatically distributed within Snowpark.
E) Use instead of 'to_pandas()'. Then, after the transformation, use 'session.write_pandas(transformed_df, 'transformed_table')'.

5. You are building a Snowpark Python application to perform complex data transformations and want to leverage external packages not pre-installed in the Snowflake environment. You need to ensure these packages are available within your Snowpark session. Which of the following methods are valid for deploying and using these third-party packages within your Snowpark Python environment? (Select TWO)

A) Deploy the required packages using the SnowCLl package management commands, and then the Snowpark session will be able to automatically use the deployed packages.
B) Utilize Snowflake's Anaconda channel integration and specify the package names as strings in the method. Snowflake will automatically resolve and install the packages from the Anaconda channel.
C) Use the 'session.addDependency()' method to upload individual '.py' files containing the package code directly to the Snowflake internal stage.
D) Create a 'conda' environment file ('environment.yml') specifying the required packages and use the method to upload the environment definition. Snowflake will automatically install the packages within the session's environment.
E) Manually install the packages on the Snowflake compute pool nodes before starting the Snowpark session.

Solutions: