A data science team is deploying a custom real-time inference service for a fine-tuned LLM using Snowpark Container Services (SPCS). They have a Docker image in their Snowflake image repository. They need to define the service using a YAML specification file. Which of the following are ''essential'' components or configurations that must be included in the 'spec.yaml'' file for a long- running service that uses this image, custom environment variables, and requires external access?

• A.
• B.
• C.
• D.
• E.

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A business is implementing Snowflake Document AI to process a high volume of scanned legal contracts. The contracts are in PDF format, average 75 pages each, and some are up to 45 MB in size. They need to extract specific clauses, contract dates, and signatory names, including any handwritten elements. The business requires the extracted data to be available for continuous processing within an automated pipeline. Which of the following considerations are critical for successful implementation of this Document AI solution?

• A.Document AI automatically detects page orientation and supports PDF as a file format. The maximum page limit of 125 pages per document and size limit of 50 MB are well within the specifications for these contracts.
• B.To train the Document AI model to extract handwritten text and signatures accurately, particularly if readability is low, the business should utilize the 'ocrScore' from the prediction results to flag documents for image quality review or enhancement.
• C.The primary method for extracting information is This method is exclusively designed for zero-shot extraction, thus fine-tuning is not an option to improve results on specific document types like these contracts.
• D.For continuous processing of new documents, the business needs to create a stream on a stage and a task to automate the execution of the extracting query. Document AI supports processing up to 500 documents in a single query.
• E.When extracting lists of values, such as multiple contract clauses, the Document AI model may initially struggle with complex extractions. It's best practice to train the model with annotations and corrections over a representative dataset to ensure accuracy and correct ordering.

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A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

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

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A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

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

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A data engineer is reviewing the purpose of AI Observability's tracing feature within Snowflake Cortex. Which of the following statements accurately describe the benefits or functionality of tracing in this context?

• A.Tracing records only the final output of an LLM inference call, not the intermediate steps.
• B.It allows debugging individual records by showing every step of application executions, including input prompts, retrieved context, and tool use.
• C.Tracing primarily focuses on aggregating performance metrics like accuracy and latency across multiple application runs for comparison, rather than step-by-step execution details.
• D.It enables refinement of the application for improved accuracy, latency, and cost.
• E.Tracing requires manual logging of each application step through custom SQL INSERT statements into an event table.

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