GES-C01 Exam Dumps | A data architect is integrating Snowflake Cortex LLM functions into various data enrichment pipelines.

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Question 33/171

A data architect is integrating Snowflake Cortex LLM functions into various data enrichment pipelines. To ensure optimal performance, cost-efficiency, and accuracy, which of the following are valid best practices or considerations for these pipelines?

A. When extracting specific entities from documents using SAI EXTRACT or '!PREDICT , it is often more effective to fine-tune a Document AI model for complex or varied document layouts rather than relying solely on extensive prompt engineering for zero-shot extraction.

B. For tasks requiring deterministic JSON outputs, explicitly specifying a JSON schema using the 'response_format' argument with 'AI COMPLETE is crucial, and for OpenAI (GPT) models, including the 'required' field and setting 'additionalPropertieS to 'false' in every node of the schema is a mandatory requirement.

C. To manage costs effectively for LLM functions like SAI COMPLETE in a pipeline, always use the largest available warehouse size (e.g., 6XL Snowpark- optimized) to maximize throughput, as this directly reduces the overall token processing time and cost.

D. When performing sentiment analysis on customer feedback using 'AI_SENTIMENT, it's best practice to pass detailed, multi-turn conversation history to the function to enhance accuracy, similar to how 'AI_COMPLETE handles conversational context.

E. For data enrichment involving classification with 'AI_CLASSIFY' , using descriptive and mutually exclusive categories in plain English, along with an optional clear task description, can significantly improve classification accuracy.

Correct Answer: A,B,E

Option A is correct. For extracting information from documents with complex or varied layouts, fine-tuning a Document AI model can significantly improve results compared to relying solely on zero-shot extraction and extensive prompt engineering. Document AI provides both zero-shot extraction and fine-tuning capabilities, with fine-tuning recommended to improve results on specific document types. Option B is correct. To ensure 'AI_COMPLETE (or 'COMPLETE) returns responses in a structured JSON format, it is essential to specify a JSON schema using the 'response_format' argument. For OpenAl (GPT) models, specific requirements include setting 'additionalPropertieS to 'false' in every node and ensuring the 'required' field lists all property names. Option C is incorrect. Snowflake explicitly recommends executing queries that call Cortex AISQL functions (such as 'AI COMPLETES) using a smaller warehouse, no larger than MEDIUM. Using larger warehouses does not increase performance for these functions but will incur unnecessary compute costs. The LLM inference itself is managed by Snowflake, and its performance isn't directly scaled by warehouse size in the same way as traditional SQL queries. Option D is incorrect. 'AI_SENTIMENT (and 'SENTIMENT) is a task-specific function designed to return a sentiment score for a given English-language text. Unlike 'AI_COMPLETE (or 'COMPLETE'), which supports multi-turn conversations by passing conversation history for a stateful experience, SAI SENTIMENT processes individual text inputs and is not designed to leverage multi-turn context in the same way for sentiment analysis. Option E is correct. For classification tasks using 'AI_CLASSIFY (or 'CLASSIFY TEXT), best practices include using plain English for the input text and categories, ensuring categories are descriptive and mutually exclusive, and adding a clear 'task_description' when the relationship between input and categories is ambiguous. These guidelines significantly improve classification accuracy.

Question 33/171

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