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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q339-Q344):
NEW QUESTION # 339
A Gen AI engineer is tasked with selecting the most suitable Large Language Model (LLM) from Snowflake Cortex AI for a new customer service chatbot. They need to rapidly prototype and compare different LLMs with varying parameters on a sample dataset before committing to a production deployment. Which of the following statements accurately describe how the Cortex Playground (Public Preview) can assist in this scenario?
- A. It provides a mechanism to deploy the chosen LLM directly into Snowpark Container Services (SPCS) compute pools from within the playground for immediate production use.
- B. It enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.
- C. It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
- D. It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.
- E. It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
Answer: B,D
Explanation:
The Cortex Playground's primary purpose is to compare text completions across multiple LLMs and test responses with different prompts and model settings to help decide which model to deploy. It explicitly states that you can connect the model to a Snowflake table with textual data for testing, processing at most 100 rows. It allows exporting SQL queries that include the defined settings, which can be executed from a worksheet or notebook, or automated with streams and tasks. However, it does not support direct fine-tuning of LLMs, nor does it provide direct deployment into SPCS; rather, it aids in the selection process *before* deployment. The export feature provides SQL, not Python code for Snowpark ML pipelines.
NEW QUESTION # 340
A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?
- A. Option A
- B. Option E
- C. Option C
- D. Option B
- E. Option D
Answer: C
NEW QUESTION # 341
An organization has implemented a strict governance policy where the 'ACCOUNTADMIN' has set the 'CORTEX MODELS ALLOWLIST' to only permit 'gemma-7b' and 'llama3.1-8b' models. A developer then executes the following SQL statements in a Snowflake worksheet using 'TRY COMPLETE (SNOWFLAKE.CORTEX)". Assuming no specific RBAC model object grants are in place for the developer's role, what would be the outcome of these queries? SELECT
- A. All three queries will return because 'TRY COMPLETE' will always prioritize strict adherence to the allowlist and any model not explicitly listed is considered unavailable.
- B. The first and third queries will return completions, but the second query will raise an error indicating an unauthorized model attempt.
- C. Only the first query will return a completion, as 'gemma-7W is the smallest and most readily available model, while the others will return 'NULL'.
- D. The first and second queries will return completions, while the third will return 'NULL' due to potential resource constraints for larger models.
- E. The first query will return a completion, the second will return 'NULL' , and the third will return a completion.
Answer: E
Explanation:
The parameter restricts which models can be used with The 'TRY_COMPLETE function executes the same operation as 'COMPLETE' but returns 'NULL' instead of raising an error when the operation cannot be performed. - The first query uses 'gemma-7b' , which is in the Therefore, it will execute successfully and return a completion. - The second query uses 'llama3.1-70b' , which is not in the configured 'CORTEX_MODELS_ALLOWLIST'. As a result, 'TRY COMPLETE will return 'NULL' because the model is not permitted by the allowlist. - The third query uses 'llama3.1-8b' , which is also in the 'CORTEX MODELS ALLOWLIST. Therefore, it will execute successfully and return a completion. Hence, option A accurately describes the outcome.
NEW QUESTION # 342
A data science team is implementing a large-scale Retrieval Augmented Generation (RAG) application on Snowflake, using 'SNOWFLAKE.CORTEX.EMBED TEXT 1024' to process millions of customer support tickets for semantic search. The goal is to achieve high retrieval quality and manage costs effectively. Which of the following are recommended practices and accurate cost/performance considerations when leveraging 'EMBED TEXT 1024' in this scenario? (Select all that apply)
- A. Even with models like 'snowflake-arctic-embed-l-v2.0-8k' which have a large context window (8192 tokens), splitting customer support tickets into chunks of no more than 512 tokens is recommended for optimal RAG retrieval quality.
- B. The function should be called using 'TRY_COMPLETE instead of directly to handle potential errors gracefully and avoid incurring costs for failed operations.
- C. For 'EMBED_TEXT 1024' , billing is based on both input and output tokens, encouraging brevity in generated embeddings to control costs.
- D. Models for such as 'snowflake-arctic-embed-l-v2.0' and 'multilingual-e5-large' , are billed at 0.05 Credits per one million input tokens processed.
- E. To minimize compute costs, the team should use a Snowpark-optimized warehouse for operations, as it is specifically designed for ML workloads.
Answer: A,D
Explanation:
Option C is correct. For best search results with Cortex Search and RAG, Snowflake recommends splitting the text into chunks of no more than 512 tokens. This practice typically results in higher retrieval and downstream LLM response quality, even for models with larger context windows like 'snowflake-arctic-embed-l-v2.0-8k' (8192 tokens). Option E is correct. For functions, only 'input tokens' are counted towards the billable total. The 'snowflake-arctic-embed-l-v2.0' and 'multilingual-e5-large' models for are indeed billed at 0.05 Credits per one million tokens. Option A is incorrect because Snowflake recommends executing queries that call Cortex AISQL functions, including 'EMBED_TEXT 1024', with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance for these functions. Snowpark-optimized warehouses are generally for ML training workloads with large memory requirements. Option B is incorrect because for ' functions, 'only input tokens' are counted towards the billable total, not output tokens. Option D is incorrect. 'TRY COMPLETE is a helper function designed for the 'COMPLETE function to return NULL on error instead of raising one, thus avoiding cost for failed 'COMPLETE' operations. There is no equivalent function mentioned in the sources, and 'EMBED TEXT 1024s is distinct from 'COMPLETE'.
NEW QUESTION # 343
A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?
- A. Option A
- B. Option E
- C. Option C
- D. Option B
- E. Option D
Answer: C
Explanation:
To provide a stateful, conversational experience with the 'COMPLETE (SNOWFLAKE.CORTEX)' function (or its latest version, 'AI_COMPLETE'), all previous user prompts and model responses must be explicitly passed as part of the argument. This argument expects an array of objects, where each object represents a turn and contains a 'role' ('system', 'user', or 'assistant') and a 'content key, presented in chronological order. In Streamlit, 'st.session_state' is the standard and recommended mechanism for storing and managing data across reruns of the application, making it ideal for maintaining chat history, by initializing 'st.session_state.messages = [l' and appending messages to it. Option A is incorrect because 'COMPLETE does not inherently manage history from external tables. Option B is incorrect as 'COMPLETE does not retain state between calls; history must be explicitly managed. Option D is a less effective form of prompt engineering compared to passing structured history, as it loses the semantic role distinction and can be less accurate for LLMs. Option E describes a non- existent parameter for the 'COMPLETE function.
NEW QUESTION # 344
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