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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineer is constructing a Retrieval Augmented Generation (RAG) pipeline in Snowflake to allow users to query a large corpus of unstructured customer support transcripts using natural language. The goal is to retrieve relevant transcript snippets and then use a Large Language Model (LLM) to generate an answer. Which sequence of steps and Snowflake components would effectively implement this RAG pipeline?

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

2. An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A) For embedding text, selecting a model like

B) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
C) CHANGE_TRACKING
D) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
E) The

3. 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 C
C) Option E
D) Option B
E) Option D

4. A company is planning to process a large volume of legal documents to generate summaries using SNOWFLAKE. CORTEX. SUMMARIZE. Given the scale, they are acutely focused on managing costs and optimizing performance. Which of the following statements are true regarding the cost and performance characteristics of using SNOWFLAKE. CORTEX. SUMMARIZE? (Select all that apply)

A) The context window for the SWIMARIZE function is 4,096 tokens, ensuring efficiency for short documents only.
B) Snowflake recommends using a larger warehouse (e.g., L or XL) for SUMMARIZE function calls to significantly improve processing performance for high-volume tasks.
C) The SUWARIZE function is billed primarily based on the number of output tokens generated in the response, not input tokens.
D) For SUWARIZE, Snowflake adds an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.
E) The fixed billing rate for the SUMMARIZE function is 0.10 Credits per one million Tokens processed.

5. A data engineering team is building a Retrieval Augmented Generation (RAG) pipeline that heavily relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT 768' to process millions of documents daily. They need to optimize for both cost and retrieval quality. Which of the following statements are true regarding the cost and performance of 'EMBED_TEXT 768' in Snowflake? (Select all that apply)

A) For optimal retrieval quality in RAG scenarios, text should be split into chunks of no more than 512 tokens before being passed to 'EMBED TEXT 768', even if the model supports a larger context window.
B) To minimize costs for ' EMBED_TEXT 768 operations, it is recommended to execute queries using a smaller virtual warehouse (no larger than MEDIUM), as larger warehouses do not improve performance for these functions.
C) The 'EMBED TEXT 768' function, regardless of the 768-dimension model used, has a fixed cost of 1.50 Credits per one million Tokens processed.
D) The 'snowflake-arctic-embed-m-vl .5 model, used by 'EMBED TEXT 768', has a context window of 512 tokens, and texts exceeding this length are truncated before embedding.
E) The 'EMBED_TEXT 768' function is billed based on the number of 'output tokens' generated by the embedding model, as this represents the computational complexity of the vector.

Solutions: