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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You've written a Snowpark stored procedure in Python that utilizes a custom Python library named 'my utils.py' containing utility functions for data transformation. This library is not available in Anaconda'. You want to deploy the stored procedure and ensure that the custom library is accessible during execution. Which is the most appropriate way to operationalize your Snowpark stored procedure with the custom library?

A) Install 'my_utils.py' on the Snowflake warehouse where the stored procedure will be executed.
B) Create a Snowpark DataFrame from 'my_utils.py' and pass it as an argument to the stored procedure.
C) Copy the contents of 'my_utils.py' directly into the stored procedure's code.
D) Package 'my_utils.py' into a wheel file and upload it to a Snowflake stage. Then, add the stage path to the 'packages' argument of the 'session.sproc.register' method.
E) Upload 'my_utils.py' to a Snowflake stage and specify the stage path in the 'imports' argument of the 'session.sproc.register' method.

2. You are using Snowpark Python to build a data pipeline. You need to version control your Snowpark application and ensure that it is compatible with different Snowflake environments (development, staging, production). Which strategies and tools would be most effective for managing the Snowpark application's code, dependencies, and deployment process?

A) Store the Python code directly in Snowflake stages and use Snowflake's versioning capabilities to manage different versions.
B) Package all Snowpark code into a single ZIP file and manually upload it to each environment.
C) Copy and paste the Python code between different Snowflake environments as needed, manually installing any required dependencies.
D) Use a Git repository to manage the Snowpark Python code, a dependency management tool like Poetry or pip to handle dependencies, and a CI/CD pipeline (e.g., using Jenkins or GitLab CI) to automate deployment to different Snowflake environments.
E) Rely solely on Snowflake's built-in Python interpreter and avoid using any external libraries or dependencies to simplify versioning and deployment.

3. You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?

A) Use Snowpark to define a series of chained UDFs that perform the transformations and aggregations directly within the Snowpipe pipeline.
B) Define a Snowpark Stored Procedure ('sproc') that is triggered automatically by a Snowflake Task whenever new data arrives via Snowpipe. The stored procedure performs the transformations and aggregations and stores the results in a new table.
C) Create a Snowpark DataFrame that represents the incoming data and use the 'write_pandaS function to write the transformed data to a separate Snowflake table after each micro-batch.
D) Continuously query the incoming data from Snowpipe using a Snowpark DataFrame and perform the transformations and aggregations on the client-side in a loop.
E) Utilize Snowflake's Streams and Tasks feature and define views with complex SQL transformations that leverages Snowpipe.

4. You are developing a Snowpark application that involves creating a set of stored procedures and UDFs to process data'. To ensure proper version control and dependency management, you decide to package your Python code into a single Python Wheel file and deploy it to Snowflake. Which of the following methods are valid for deploying and utilizing this Python Wheel file within Snowflake, considering best practices for maintainability and security? (Select TWO)

A) Use the 'snowflake-cli' to push the Python Wheel file as a package, then add the package name to the list of packages when creating the stored procedure or UDF.
B) Use the Snowsight UI to upload the Python Wheel file as a dependency for the Snowflake environment, making it available for all stored procedures and UDFs within that environment.
C) Upload the Python Wheel file to an internal stage and directly reference it in the UDF or stored procedure definition using the 'USING' clause.
D) Upload the Python Wheel file to an external stage (e.g., AWS S3) and configure Snowflake to access the external stage, then reference the wheel file path in the USING' clause of the UDF or stored procedure.
E) Create a Conda environment file (environment.yml) that specifies the Python Wheel file as a dependency and use this file to create a Snowflake environment. Then, associate the stored procedures and UDFs with that environment.

5. You are migrating a Pandas-based data processing pipeline to Snowpark to leverage Snowflake's scalability and performance. One part of the pipeline involves a computationally intensive custom function that is applied row-by-row to a DataFrame using the 'apply' method in Pandas. When migrating this to Snowpark, what are the most effective strategies for achieving similar functionality while maximizing performance within the Snowflake environment?

A) Create a Snowpark User-Defined Function (UDF) using Python and apply it to the DataFrame using the 'select method, leveraging Snowflake's distributed execution capabilities.
B) Rewrite the custom function as a vectorized operation using Snowpark DataFrame functions and expressions, avoiding row-by-row processing.
C) Use a stored procedure to execute the pandas 'apply' row by row on the data from snowflake table.
D) Directly translate the Pandas 'apply' operation to a Snowpark 'apply' operation, assuming that Snowpark's implementation is automatically optimized for distributed execution.
E) Utilize Snowpark's Pandas API to seamlessly execute the Pandas code within the Snowflake environment with minimal modifications.

Solutions: