A data scientist fine-tuned a mistral-lb model in Snowflake for a specific customer support response generation task, naming it my_custom_responder_model. They now want to make this model available for AI_COMPLETE calls in production, ensuring proper access control and regional availability. Which of the following statements is true regarding the deployment and management of this fine-tuned model in Snowflake?

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

A team is developing a Retrieval Augmented Generation (RAG) pipeline in Snowflake, where document chunks are embedded using Cortex AI functions and then retrieved using VECTOR_COSINE_SIMILARITY They are planning their infrastructure and cost management strategy. Which of the following statements correctly describes the cost or performance characteristics of these operations in Snowflake? (Select all that apply)

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

An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

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

A marketing analyst wants to quickly gauge the overall sentiment of customer feedback stored in a Snowflake table called CUSTOMER_FEEDBACK, which has a column FEEDBACK_TEXT. They decide to use the SNOWFLAKE .CORTEX.SENTIMENT function to process a review. Consider the following SQL query for a specific review:

Which of the following correctly describes the expected output format and interpretation of the sentiment_score for the given input?

• A. The output will be a string like 'Positive' or 'Negative', and a score close to 1 indicates strong positive sentiment.
• B. The output will be a floating-point number between -1 and 1 (inclusive), where a value of 1 indicates strong positive sentiment and -1 indicates strong negative sentiment.
• C. The output will be a JSON object containing a 'label' field, and values around 0 indicate a neutral sentiment.
• D. The output will be a boolean value (TRUE/FALSE) indicating if the sentiment is positive, and 0.5 represents a neutral sentiment.
• E. The output will be an integer between 0 and 1 00, where higher values denote more positive sentiment.

A financial institution uses Snowflake to process an average of 5 million PDF documents (average 10 pages each) monthly for regulatory compliance using AI_PARSE_DOCUMENT in LAYOUT mode. They are monitoring costs and considering performance optimizations. Given this scenario, which of the following statements about cost and performance for AI_PARSE_DOCUMENT are true?

• A. If a document is a HTML file, the billing is based on processing each chunk of 3,000 characters as a page.
• B. The primary billing metric for in LAYOUT mode is the number of tokens processed, which includes both input and output tokens.
• C. To reduce costs, the team should aim to process documents in batches, ensuring each query processes a maximum of 1 ,000 documents to avoid errors.
• D. Selecting a 'LARGE' or 'X-LARGE' standard warehouse will significantly improve the processing speed and reduce the per-page cost for AI_PARSE_DOCUMENT.
• E. For optimal results with multilingual documents, the LAYOUT mode supports a broader range of languages than OCR mode.