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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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 SUWARIZE function is billed primarily based on the number of output tokens generated in the response, not input tokens.
B) The fixed billing rate for the SUMMARIZE function is 0.10 Credits per one million Tokens processed.
C) 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.
D) The context window for the SWIMARIZE function is 4,096 tokens, ensuring efficiency for short documents only.
E) Snowflake recommends using a larger warehouse (e.g., L or XL) for SUMMARIZE function calls to significantly improve processing performance for high-volume tasks.

2. A machine learning team has fine-tuned a llama3.1-70b model for a specialised task using Snowflake Cortex Fine-tuning, named prod_llama_responder. They now need to deploy this model for inference via the Cortex REST API across different Snowflake regions and manage its lifecycle effectively. Which of the following statements regarding the fine-tuned model's deployment, access, and management are accurate?

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

3. A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses 'invoice_model!PREDlCT' 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 D
D) Option C
E) Option A

4. A Snowflake administrator is tasked with monitoring and optimizing costs for various Gen AI applications leveraging Snowflake Cortex LLM functions. They need to generate a report detailing token consumption for individual API calls to identify high-usage patterns and specific models. Which of the following Snowflake account usage views or methods would provide the most granular insights into prompt, completion, and guardrail token usage for Cortex LLM function calls?

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

5. A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

A) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
B) The
C) Using the
D) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
E) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the

Solutions: