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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with deploying a Snowpark Python application that utilizes a third-party library, 'scikit-learn' , for machine learning tasks. The application will be executed as a Snowflake Stored Procedure. What are the necessary steps to ensure the 'scikit-learn' library is available within the Snowpark environment?

A) Include the 'scikit-learn' library directly in the Snowpark session using 'session.add_import(sklearny.
B) Install scikit-learn on your local machine, package your snowpark code into a zip file and upload it to a stage, no extra steps are required.
C) Upload the 'scikit-learrf library as a ZIP file to a Snowflake stage, create a Python UDF that unzips the library, and then import the library within the Snowpark Stored Procedure.
D) Create a Snowflake Anaconda environment using conda, include the 'scikit-learns package in the environment, and then create a Snowpark Stored Procedure that utilizes the environment via the 'packages' parameter in the CREATE PROCEDURE statement.
E) Create a Snowflake Anaconda channel integration, add the 'scikit-learn' package to the channel, and then reference the channel in the Snowpark session configuration.

2. You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame named 'transactions' containing transaction data'. You need to create a UDTF using Python to categorize transactions into 'High Value', 'Medium Value', and 'Low Value' based on the transaction amount and the customer's region. The categorization logic requires access to a dynamically updated lookup table stored in a Snowflake stage. Which approach would be MOST efficient and scalable, minimizing data transfer and maximizing Snowpark's vectorized operations?

A) Use a scalar UDF, reading the lookup table from the stage for each transaction. This ensures data consistency but may incur significant overhead for each row processed.
B) Create a vectorized UDF. Load the lookup table from the stage into the UDF's environment once during initialization. Then, process transactions in batches using pandas DataFrames within the UDF.
C) Define a scalar UDF that queries the lookup table directly from Snowflake using a Snowflake connector. This avoids data transfer to the UDF but introduces external dependency and connection management overhead for each row.
D) Use a UDTF with the parameter, reading the lookup table directly into the UDTF using a Snowpark DataFrame and joining it with each batch of the 'transactions DataFrame. Materialize the result to a temporary table.
E) Create a vectorized UDTF that loads the lookup table into memory during the first call, and then caches it for subsequent calls. Implement a refresh mechanism using a Snowflake external function triggered by stage updates.

4. You have a Snowpark Python application that uses a UDF to perform custom data transformations. The UDF relies on a large, read-only lookup table that is stored as a CSV file on a Snowflake stage. Which of the following strategies would be the MOST efficient way to access the lookup table within the UDF?

A) Load the CSV file into a Snowflake table and then query the table from within the UDF using 'session.sql(V.
B) Read the CSV file from the stage every time the UDF is called using 'snowflake.connector.connect()' and then load the data into a Pandas DataFrame within the UDF function.
C) Load the CSV file into a Snowflake stage, and in the python UDF code, use the get_stage_file API from session object to read the file once. Then the data cached in-memory within the UDF module, and reuse the cached data for subsequent calls.
D) Use the 'cachetoolS library with a Least Recently Used (LRU) cache to store the lookup table in memory. The UDF will check the cache before reading the CSV file, and update the cache if necessary. The CSV file is read with get_stage_file API from session.
E) Read the CSV file from the stage once when the UDF is first called, cache the data in a global variable within the UDF module, and then reuse the cached data for subsequent calls.

5. You are tasked with deploying a set of Python UDFs and UDTFs to a Snowflake environment using Snowpark. These functions rely on several external Python packages and need to be versioned and managed effectively. Which of the following strategies provides the MOST robust and scalable solution for managing dependencies and deploying these functions in a reproducible manner?

A) Manually uploading the required Python packages to a Snowflake stage and specifying them in the 'packages' argument of the '@sf.UDF and 'session.udtf.register' calls. Update the packages on stage every time dependencies are upgraded.
B) Creating a 'requirements.txt' file and including all required packages in it. Zipping this file and uploading it to a Snowflake stage, and specifying it in 'imports'.
C) Options A,B and C are all equally viable options
D) Creating a conda environment specification file (environment.yml) that lists all dependencies, storing the environment.yml file in a Snowflake stage. Update the environment.yml when dependencies are upgraded.
E) Creating a 'requirements.txt' file, using 'conda' to create an environment.yml and use 'snowflake.snowpark.functions.udf and 'session.udtf.register' with the environment.yml. Update the environment.yml manually when dependencies are upgraded.

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