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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Python function that takes a string as input and returns a sentiment score (a float between -1 and 1). This function relies on a large pre-trained Natural Language Processing (NLP) model. You want to deploy this function as a UDF in Snowpark and optimize its performance, specifically minimizing the model loading time for each execution. You have already uploaded the model to a stage named '@my_stage/models'. Select the option that combines caching techniques and UDF deployment strategies to achieve the best performance.

A)

B)

C) Option B and C are best and gives a scalable solution
D)

E)

2. You have a Python function named 'process data' that performs data cleaning and transformation on a Pandas DataFrame. You want to convert this function into a Snowpark Python stored procedure to leverage Snowflake's compute resources. However, the 'process_data' function relies on several external Python libraries (e.g., 'pandas', 'numpy', 'scikit-learn') that are not pre-installed in the Snowflake environment. Which of the following approaches would ensure that these dependencies are available within the Snowpark stored procedure? Choose all that apply

A) Create a custom Anaconda environment with the required packages and upload it to a Snowflake stage. Then, specify the stage location in the 'imports' argument of the 'CREATE PROCEDURE statement.
B) Bundle the required libraries into a ZIP file and upload it to a Snowflake stage. Then, add the ZIP file to the 'imports' list during stored procedure creation, ensuring that the library paths are correctly referenced within the Python code.
C) Specify the required packages in the 'packages' argument of the '@sproc' decorator or 'session.add_packageS method.
D) Use session.custom_package to resolve dependencied
E) Include the necessary 'import' statements for the libraries within the stored procedure's code. Snowflake will automatically resolve and install the dependencies.

3. Consider the following Snowpark Python code snippet designed to create a DataFrame and then register a custom function (UDF):

This code runs successfully. However, you need to deploy this as a stored procedure. What minimal changes are required to make this code runnable as a Snowpark Python stored procedure and callable from SQL?

A) The 'return df.collect()' line must be replaced with 'return and the 'return_type' and 'input_typeS arguments of udf must be removed to allow inference. The rest of the code remains unchanged.
B) The 'return df.collect()' line must be replaced with 'return and 'return_type' and 'input_typeS arguments of udf must be removed. The rest of the code remains unchanged.
C) The 'return df.collect()' line must be replaced with 'return and the Snowflake session object must be explicitly passed to the UDF when it is called.
D) The 'return df.collect()' line must be replaced with 'return df and create a DataFrame. The rest of the code remains unchanged.
E) No changes are required; the code will function as a stored procedure as is.

4. You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame representing customer transactions. This DataFrame is used in multiple downstream operations within your Snowpark application. Which of the following strategies would be MOST effective for optimizing the performance of these downstream operations by materializing the results of the 'df DataFrame, and what considerations should be made regarding resource usage?

A) Create a temporary table using 'df.write.save_as_table('temp_transactions', temporary-True)'. This persists the DataFrame to Snowflake storage, reducing the need for repeated computations. Monitor the size of the temporary table and its impact on storage costs.
B) Use to materialize the DataFrame in memory. This is the most efficient approach as it minimizes disk I/O. Consider the size of the DataFrame relative to available memory to avoid memory pressure.
C) Use 'df.checkpoint()' to truncate the DataFrame lineage. This will prevent re-computation in any downstream operations. Monitor the impact on storage costs.
D) Using a local variable to store the DataFrame. This method is most suitable for materializing the results of the DataFrame.
E) Write the DataFrame to a persistent Snowflake table using and then read it back into a new DataFrame. This ensures data persistence but may introduce overhead due to data serialization and deserialization. Only use this method if persistence is required beyond the session.

Solutions: