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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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 B
B) Option E
C) Option D
D) Option C
E) Option A

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) 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.

3. A development team is implementing a suite of generative AI applications on Snowflake, utilizing both SQL functions and the Cortex REST API. They prioritize content safety and plan to integrate Cortex Guard wherever possible. Considering the various interfaces for interacting with Snowflake Cortex LLMs, which of the following interfaces and functions support the direct use of Cortex Guard via the guardrails' argument or equivalent configuration?

A) The 'Cortex Playground' (Public Preview) when testing prompts and model settings.
B) The 'SNOWFLAKE.CORTEX.COMPLETE SQL function for generative AI tasks.
C) The 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT SQL function for text classification tasks.
D) The 'SNOWFLAKCORTEX.TRY_COMPLETE SQL function, which is the error-tolerant version of 'COMPLETE.
E) The Snowflake Cortex LLM REST API when invoking the '/api/v2/cortex/inference:complete' endpoint.

4.

A)

B)

C)

D)

E)

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) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
B) The
C) 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.
D) 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.
E) 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

Solutions: