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

2. A Snowflake team observes consistently high token costs from 'SNOWFLAKE.ACCOUNT USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY' for a summarization task using the 'mistral- large? model. The task involves summarizing legal documents, which often exceed the context window of common LLMs. To optimize these token-based costs, which strategy should the team prioritize?

A) Increase the virtual warehouse size (e.g., from X-SMALL to MEDIUM) used for running the summarization queries to boost performance and reduce overall cost per query.
B) Set the temperature parameter to 0 in the COMPLETE function options to ensure more deterministic and thus more cost-efficient summarization outputs.
C) Enable Cortex Guard for the COMPLETE function calls, as its filtering capabilities automatically reduce the number of tokens processed for unsafe content.
D) Implement a text splitting mechanism, potentially using SPLIT_TEXT_RECURSIVE_CHARACTER, to break down lengthy documents into smaller chunks before passing them to the summarization function, then aggregate the summaries.
E) Switch from using the COMPLETE function to TRY_COMPLETE to automatically avoid billing for queries that fail due to context window limits, thereby reducing costs.

3. A project team is preparing to deploy a Document AI solution to process scanned customer feedback forms. They have created a dedicated role, 'customer feedback _ processor', and successfully granted it the SNOWFLAKE. DOCUMENT_INTELLIGENCE_CREATOR database role. The environment consists of 'feedback database, 'forms schema' schema, and 'ai workload warehouse. However, when the attempts to prepare a Document AI model build in Snowsight, they encounter a 'permission denied' error. Which of the following missing 'USAGE' grants could be the direct cause of this error?

A)

B)

C)

D)

E)

4. 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) The
B) For embedding text, selecting a model like

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

5. An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?

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

Solutions: