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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A global analytics firm is developing a Retrieval Augmented Generation (RAG) system in Snowflake to answer customer queries across a large repository of technical documentation, which includes documents in English, German, and Spanish. They are looking to use a Snowflake Cortex embedding model to convert document chunks into vector embeddings for their Cortex Search Service. Which of the following considerations are critical when selecting an appropriate embedding model to optimize for both query relevance and cost-efficiency for their multilingual RAG application? (Select all that apply)

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

2. A company is using Snowflake AI Observability to evaluate a summarization application. The application utilizes SNOWFLAKE. CORTEX. COMPLETE for LLM inference and a custom Python component for text pre-processing. The team is particularly interested in tracking detailed cost breakdowns and assessing the factual correctness of the LLM-generated summaries. Which of the following statements accurately describe the cost implications and evaluation metric capabilities in this scenario?

A) AI Observability incurs charges for the LLM judges invoked via COMPLETE (SNOWFLAKE .CORTEX) calls to compute evaluation metrics, in addition to warehouse charges for managing runs and queries.
B) To measure the factual correctness of LLM-generated summaries based on original input and avoid hallucinations, the 'factual correctness' and 'comprehensiveness' metrics can be used during evaluations.
C) The ORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view is the primary tool to monitor the credit consumption specifically for AI Observability evaluations and LLM judge usage.
D) For evaluating summarization tasks, the 'context relevance' score is the most important metric, as it directly assesses the quality of the LLM's output against the source document.
E) The cost of AI Observability is primarily determined by the number of messages processed, and the number of tokens in each message does not affect the cost, ensuring predictable pricing.

3. A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses to extract data from daily financial reports. They observe that their assigned 'LARGE virtual warehouse is running continuously, even during periods of low document ingestion, contributing significantly to their bill. They want to investigate how to reduce costs effectively for their existing Document AI setup.

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

4. A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

A) When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
B) The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
C) Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.
D) Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.
E) Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.

5. A developer is building an interactive chat application in Snowflake leveraging the COMPLETE (SNOWFLAKE. CORTEX) LLM function to power multi-turn conversations. To ensure the LLM maintains conversational context and generates coherent responses based on prior interactions, which of the following methods correctly implements the passing of conversation history to the COMPLETE function?

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

Solutions: