A developer is refining a Document AI extraction process using the '!PREDICT' method and is meticulously examining the JSON output for invoices, which include 'invoice number', 'invoice items', 'tax amount', and 'vendor name'. They also have a detailed internal table of 'product details' to be extracted. To ensure optimal data quality and accurate interpretation of the extracted information, which of the following best practices or characteristics of Document AI's output should the developer consider?

• A. To maximize accuracy when defining data values, questions should be broadly generic (e.g., 'What is the amount?) to allow the Document AI model to infer the most relevant context, especially for fields like 'tax_amount' where multiple numbers might be present.
• B. When extracting lists of values, such as 'invoice_items', the Document AI model returns them as an array in the JSON output, preserving the original order of items as they appear in the document.
• C. If the 'vendor_name' field cannot be confidently identified in a document, the model will include '"vendor_name": [ { "score": O.X, "value": "NOT FOUND" } l' in the JSON output.
• D. The 'ocrScore' provided in the '_documentMetadata' object for each document indicates the model's confidence in the content of specific extracted values, rather than the overall quality of the optical character recognition process.
• E. For table extraction, such as the extracted values for each column (e.g., 'tablel litem', 'tablel Igross) are ordered consistently with the rows of the original table, facilitating direct joining of columns.

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

A data engineering manager needs to audit Cortex LLM function costs to identify specific SQL queries that are unexpectedly high in token consumption for the 'llama3.1-8b' model. They require granular analysis of prompt, completion, and guardrail token usage for these queries. Which of the following Snowflake methods or views would provide the necessary insights?

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

A machine learning team is leveraging the Snowflake Model Registry to manage diverse models, including a custom Python utility for data preprocessing that they wish to make available as a model method. Which of the following statements accurately describe capabilities or considerations when logging models and their associated artifacts and methods in the Model Registry?

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

An organization is planning to deploy Snowflake Cortex Agents for sensitive financial reporting, requiring strict adherence to data governance policies and clear understanding of cost drivers. Which of the following statements about governance and cost considerations for Cortex Agents are true?

• A. The CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view provides detailed credit consumption for Cortex Agent activities, including orchestration steps and tool usage.
• B. To use a semantic model with Cortex Agents, the role executing the agent request requires only the SNOWFLAKE .CORTEX_AGENT_USER role, as it implicitly inherits all necessary privileges for semantic model access.
• C. Cortex Agents are powered by Snowflake-hosted LLMs by default, ensuring that all customer data and prompts remain within Snowflake's governance boundary.
• D. Usage costs for Cortex Agents are primarily driven by the number of tokens processed by the underlying LLMs for orchestration and the compute/service costs of tools like Cortex Analyst and Cortex Search.
• E. Monitoring of Cortex Agent interactions for debugging and refinement is exclusively performed through internal Snowflake system logs, with no external SDK support.