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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A development team is constructing a Gen AI application using Snowflake Cortex LLM functions, particularly for conversational and text generation tasks. They are concerned about potential high costs due to token consumption. Which of the following strategies would most effectively help minimize token usage and optimize costs when working with these Cortex LLM functions?

A) Utilize the COUNT_TOKENS (SNOWFLAKE .CORTEX) helper function to pre-validate the prompt length against the model's context window, thereby preventing truncation errors and subsequent re-runs.
B) When employing AI_COMPLETE for structured output tasks, providing concise and highly descriptive explanations for each field within the JSON schema will reduce the input tokens required for the LLM to understand and adhere to the schema accurately.
C) In multi-turn conversations within Cortex Analyst, integrate a dedicated LLM summarization agent to rephrase follow-up questions, which reduces the total conversational history passed as context to the main LLM.
D) To encourage more succinct LLM responses and reduce completion_tokens, configure the temperature option to a higher value (e.g., 0.7) in COMPLETE function calls.
E) For multi-turn conversational experiences using SNOWFLAKE .CORTEX.COMPLETE, only send the most recent user prompt in each API call, as the model automatically retains previous context.

2. 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) 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.
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) 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.
D) 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.
E) 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.

3. An ML Engineer is logging a custom PyCaret model to the Snowflake Model Registry, with the intention of deploying it to Snowpark Container Services (SPCS) for GPU-powered inference. The PyCaret model is wrapped in a 'custom_model.ModelContext'. Which of the following statements correctly describe the considerations for the call and the model's environment?

A) Option B
B) Option A
C) Option D
D) Option C
E) Option E

4. A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?

A) Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.
B) Primary purpose is to translate text. Both document types should use LAYOUT mode.
C) Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
D) Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
E) Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.

5. A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

A) For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like

B) The

C) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
D) Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
E) To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.

Solutions: