You are developing a Python stored procedure in Snowflake to predict sales for a retail company. You want to incorporate external data (e.g., weather forecasts) into your model. Which of the following methods are valid and efficient ways to access and use external data within your Snowflake Python stored procedure?

• A. Load the external data into a Snowflake table and then query the table from within the Python stored procedure using the Snowflake Connector for Pythom
• B. Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.
• C. Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
• D. Use a Snowflake external function to pre-process the external data and then pass the processed data as input parameters to the Python stored procedure.
• E. Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.

You are using Snowpark to build a collaborative filtering model for product recommendations. You have a table 'USER_ITEM INTERACTIONS with columns 'USER ID', 'ITEM ID', and 'INTERACTION TYPE'. You want to create a sparse matrix representation of this data using Snowpark, suitable for input into a matrix factorization algorithm. Which of the following code snippets best achieves this while efficiently handling large datasets within Snowflake?

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

You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

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

You are using Snowflake Cortex to build a customer support chatbot that leverages LLMs to answer customer questions. You have a knowledge base stored in a Snowflake table. The following options describe different methods for using this knowledge base in conjunction with the LLM to generate responses. Which of the following approaches will likely result in the MOST accurate, relevant, and cost-effective responses from the LLM?

• A. Partition your database by different subject matter and then query the specific partitions for your information.
• B. Use Retrieval-Augmented Generation (RAG). Generate vector embeddings for the knowledge base entries, perform a similarity search to find the most relevant entries for each customer question, and include those entries in the prompt.
• C. Directly prompt the LLM with the entire knowledge base content for each customer question. Concatenate all knowledge base entries into a single string and include it in the prompt.
• D. Use Snowflake Cortex's 'COMPLETE function without any external knowledge base. Rely solely on the LLM's pre-trained knowledge.
• E. Fine-tune the LLM on the entire knowledge base. Train a custom LLM model specifically on the knowledge base data.

You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the 'REVIEW EMBEDDINGS' table has columns 'review_id' and 'embedding' (a VECTOR column) and query_embedding' is a pre-computed vector embedding?

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