Consider the following Snowpark Python code snippet designed to read data from a Snowflake table, apply a user-defined function (UDF) for data transformation, and then write the transformed data to another table. The UDF, 'calculate_score' , requires a configuration file ('config.json') to be loaded. Which of the following code snippets demonstrates the CORRECT and MOST efficient way to load and access the "config.json' file within the UDF, ensuring that it's available to all UDF invocations without requiring network access?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

• A. Convert the scalar UDF to a vectorized UDF, even without fully understanding the source of the performance bottleneck.
• B. Implement caching for the UDF's results to avoid recomputing the same values multiple times.
• C. Increase the warehouse size and re-run the application. If the execution time improves significantly, the issue was resource contention.
• D. Replace the Python UDF with an equivalent SQL query using Snowflake's built-in functions. If the SQL query performs better, the Python UDF was the bottleneck.
• E. Use Snowflake's query profile to examine the execution plan and identify the UDF-related stages with the highest execution time. Then, analyze the UDF's code for inefficiencies, such as unnecessary loops or complex calculations.

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. 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.
• B. 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.
• C. 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'.
• D. Options A,B and C are all equally viable options
• E. 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.

You have a Python dictionary 'data' representing configuration settings for your Snowpark application. You need to convert this dictionary into a Snowpark DataFrame with a single row and two columns named 'Setting' and 'Value'. The 'Setting' column should contain the keys from the dictionary, and the 'Value' column should contain the corresponding values. The DataFrame needs to be created efficiently and ensure string representation of both the setting and value. Which approach is most suitable, ensuring correctness and conciseness?

• A. python import snowflake.snowpark.types as T settings = list(data.items()) schema = T.StructType([T.StructField('Setting', T.StringType()), T.StructField('Value', T.StringType())]) df = session.createDataFrame(settings, schema=schema)
• B. python import pandas as pd pd_df = pd.DataFrame(data.items(), columns=['Setting', 'Value')) df = session.createDataFrame(pd_df)
• C. 'python settings = [1k, v] for k, v in data.items()] df = session.createDataFrame(settings, schema=['Setting', 'Value'])
• D. 'python settings = [{'Setting': k, 'Value': v} for k, v in data.items()] df = session.createDataFrame(settings)
• E. python settings = [1k, str(v)] for k, v in data.items()] schema = ['Setting', 'Value'] df = session.createDataFrame(settings, schema=schema)

A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.

• A. The size of the data being processed exceeds Snowflake's memory limits, causing transformations to be skipped.
• B. Snowpark employs lazy evaluation to optimize query execution by delaying the execution of transformations until the results are actually required.
• C. The 'eager_execution' session parameter is set to 'True'.
• D. Snowpark operations are only executed when an action (e.g., 'collect()', 'show()', is called on the DataFrame or when the DataFrame is materialized.
• E. Snowpark automatically executes all transformations as soon as they are defined, regardless of whether the results are needed.