You have a Snowpark DataFrame named with the following schema: 'product_id' (INTEGER), (STRING), 'category' (STRING), 'price' (FLOAT), and 'description' (STRING). You want to perform several data cleaning and transformation steps. Which of the following operations can be efficiently chained together using Snowpark DataFrames to clean null values in 'description', replace special characters in 'product_name' and standardize 'category' values? Select all that apply:

• A. Using the function to remove special characters (e.g., '$', '#, '@') from the 'product_name' column using a regular expression.
• B. Using the method to replace null values in the 'description' column with a default string 'No description available'.
• C. Using the 'coalesce' function to fill null values in 'description' with values from a separate 'backup_description' column (if available).
• D. Using a UDF (User-Defined Function) written in Python to standardize the 'category' column by converting all values to lowercase and removing leading/trailing spaces.
• E. Manually iterating through each row of the DataFrame and applying Python string manipulation functions to clean the data. (e.g. row['description'] =

Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

• A. The 'filter' operation Ccol('column_a') > 100') will be executed twice.
• B. The 'countl' operation will trigger the materialization and caching of 'filtered_df.
• C. The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.
• D. Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
• E. The 'count? operation will use the cached results of and apply an additional filter on the cached data.

You have developed a Python function that performs complex data transformation on customer data'. You want to operationalize this function as a UDTF in Snowpark to process large datasets efficiently. The function takes a customer ID and a list of transaction amounts as input and returns a table with calculated risk scores for each transaction. Which of the following code snippets correctly defines and registers this UDTF in Snowpark, ensuring proper type handling and scalability?

• A.
  
• B. Options B and C are the most correct.
• C.
  
• D.
  
• E.
  

You are working with a Snowpark DataFrame containing website traffic data'. The DataFrame has columns like 'date' , 'page_url', and 'visit_count'. You need to calculate the cumulative sum of visit counts for each 'page_url' over time (i.e., ordered by 'date'). However, you only want to consider data from the last 30 days for each calculation. Which of the following Snowpark code snippets will correctly achieve this using window functions with a frame specification?

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

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 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.
• B. 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.
• C. 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.
• D. 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.
• E. Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.