A Snowflake administrator is tasked with monitoring and optimizing costs for various Gen AI applications leveraging Snowflake Cortex LLM functions. They need to generate a report detailing token consumption for individual API calls to identify high-usage patterns and specific models. Which of the following Snowflake account usage views or methods would provide the most granular insights into prompt, completion, and guardrail token usage for Cortex LLM function calls?

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

Correct Answer: B,D

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A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?

• A.Scale down the virtual warehouse to 'X-SMALC or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
• B.Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
• C.Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
• D.Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE'Y.
• E.Increase the 'max_tokenS parameter within the ' !PREDICT' function options to accommodate longer document responses from the model.

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A data engineer is building an AI data pipeline to automatically extract specific sentiment categories from customer reviews using 'AI_COMPLETE. They want the output to be a structured JSON object containing 'food_quality', 'food_taste', 'wait_time', and 'food cost' with their respective sentiments (e.g., 'positive', 'negative', 'neutral'). The engineer aims for high accuracy and ensures that all these fields are present in the output. Which of the following statements correctly describe the approach to achieve this?

• A.The 'response_format' argument in 'AI COMPLETE should specify a JSON schema, and to ensure all fields are present, the 'required' field must be explicitly set for all desired properties in the schema.
• B.For optimal accuracy in structured output, it is always best to explicitly instruct the LLM within the prompt to 'Respond in JSON' and provide a detailed description of the schema, regardless of task complexity.
• C.When using OpenAl (GPT) models for structured output, the 'additionalPropertieS field must be set to 'true' in every node of the schema to allow for flexibility in the generated JSON.
• D.To improve accuracy, the JSON schema should include detailed descriptions for each field (e.g., 'food_quality', 'food_taste') to guide the model more precisely in identifying and extracting the sentiments.
• E.The 'temperature' option should be set to a high value (e.g., 0.9) in the 'CompleteOptionS to encourage the model to strictly adhere to the specified JSON schema and reduce non-deterministic responses.

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A data platform administrator needs to retrieve a consolidated overview of credit consumption for all Snowflake Cortex AI functions (e.g., LLM functions, Document AI, Cortex Search) across their entire account for the past week. They are interested in the aggregated daily credit usage rather than specific token counts per query. Which Snowflake account usage views should the administrator primarily leverage to gather this information?

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

Correct Answer: B

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A data engineer is designing an automated pipeline in Snowflake to process streaming customer support tickets using LLM functions. To ensure the pipeline is robust against unexpected LLM errors (e.g., due to malformed input or transient service issues), they decide to use SNOWFLAKE.CORTEX.TRY_COMPLETE instead of SNOWF LAKE .CORTEX.COMPLETE
. Which of the following is the most significant advantage of using
TRY_COMPLETE
in this scenario?

• A.It provides a built-in retry mechanism for LLM calls that encounter temporary failures, automatically re-submitting the prompt up to three times.
• B.It processes tokens at a significantly reduced rate compared to
• C.It returns
• D.It automatically adjusts the LLM's
• E.It offers enhanced security features, such as automatic input sanitization, which are not available in the standard

Correct Answer: C

Option C is correct because

instead of raising an error when the operation cannot be performed. This feature is crucial for building robust data pipelines, as it allows the pipeline to gracefully handle individual LLM call failures and continue processing other records, preventing the entire task from halting. Option A is incorrect; the sources do not mention an automatic retry mechanism for

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