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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
B) 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.
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) 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.
E) 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.

2. You are tasked with optimizing a Snowpark application that performs sentiment analysis on customer reviews using a Python UDE The UDF uses a large pre-trained natural language processing (NLP) model stored in a file named 'sentiment_model.pkl'. The current implementation loads the model from the stage for each row of data processed, which is impacting performance. How can you optimize the application to load the model only once per worker process?

A) Use a global variable to store the loaded model. Load the model from the stage into the global variable only if it is currently None. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
B) Use to import 'sentiment_model.pkl'. Use the decorator from the 'functools' module to cache the model loading function, initializing the model outside of the UDF definition.
C) Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
D) Implement a custom initialization function that loads the model and is called only once per worker process. Utilize the to retrieve and cache model during session initialization. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
E) Use the decorator from the 'functools' module to cache the model loading function. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.

3. You are tasked with optimizing a Snowpark Python stored procedure that performs complex data transformations on a DataFrame. The procedure frequently encounters out-of-memory errors when processing large datasets. Which of the following strategies could you implement to mitigate these memory issues within the stored procedure's code ? Choose all that apply.

A) Utilize the 'repartition()' or functions to control the number of partitions in the DataFrame and potentially reduce memory consumption per partition.
B) Increase the warehouse size to provide more memory resources.
C) Leverage the 'sample()' function to work with a smaller subset of the data for testing and debugging.
D) Implement data filtering and aggregation as early as possible in the transformation pipeline to reduce the size of the DataFrame.
E) Use smaller data types (e.g., ' Int16' instead of ' Int64') where appropriate to minimize memory footprint.

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

5. You are developing a Snowpark application that uses a UDF written in Python. This UDF requires several third-party Python packages (e.g., 'pandas', 'scikit-learn'). You need to ensure these packages are available in the UDF's execution environment within Snowflake. You have the packages listed in a 'requirements.txt' file. Which of the following methods, alone or in combination, correctly provision these dependencies for the UDF? (Select all that apply)

A) Include the packages directly in the '@udf decorator's 'packages' argument as strings (e.g., 'scikit-learnT).
B) Upload the 'requirements.txt file to a Snowflake stage and specify the stage location in the 'imports' argument of the '@udf decorator.
C) Use the 'session.add_packages' method to add the packages programmatically before registering the UDF.
D) Manually install the packages on the Snowflake compute warehouse nodes using SSH.
E) Create a Conda environment with the required packages and upload the resulting environment file to a Snowflake stage. Specify the stage location in the 'conda_integration' argument of the 'session.udf.register' method or guff decorator.

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