New Launch Snowflake GES-C01 Exam Questions Are Out: Download And Prepare

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q256-Q261):

NEW QUESTION # 256
A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

Answer: A,B,C

Explanation:
Option A is correct because Cortex Search is a fully managed service that gets users started with a hybrid (vector and keyword) search engine on text data in minutes, without needing to worry about embedding, infrastructure maintenance, or index refreshes. Option B is incorrect because Cortex Search is a fully managed service; users do not need to manually manage the embedding model infrastructure. A default embedding model is used if not specified. Option C is correct because, for best search results with Cortex Search, Snowflake recommends splitting text into chunks of no more than 512 tokens, as smaller chunks typically lead to higher retrieval and downstream LLM response quality, even with models that have larger context windows. Option D is correct because the 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' function allows users to test the search service to confirm it is populated with data and serving reasonable results for a given query. Option E is incorrect because change tracking is required on the source table for the Cortex Search Service to function correctly and reflect updates to the base data.


NEW QUESTION # 257
A data application developer is tasked with creating a multi-turn conversational AI application using Streamlit in Snowflake (SiS), which will leverage Snowflake Cortex LLM functions. Considering the core requirements for building such an interactive chat interface and the underlying Snowflake environment, which of the following actions is a fundamental step in setting up the application for stateful conversations?

Answer: D

Explanation:
For a multi-turn conversational AI application built with Streamlit, maintaining the conversation history is fundamental. Streamlit's st.session_state' is the primary way to store and manage state across reruns of the application, which is crucial for remembering past interactions in a chat interface. The typical approach involves initializing 'st.session_state.messages' to an empty list and appending messages for each turn. Option A is incorrect because is a database role specific to Document AI, not general Cortex LLM functions. Option C is not a fundamental step for running a Streamlit application in Snowflake (SiS) itself, as SiS directly hosts the Streamlit app; while models can be served via SPCS, the application itself doesn't inherently require it for basic operation. Option D is related to cross-region inference for LLM functions, which controls where inference requests are processed, not a fundamental step for local execution or conversational state management. Option E suggests a configuration ("ON ERROR':'SKIP") that is primarily used with Snowflake ML functions like Anomaly Detection and Time-Series Forecasting to prevent overall training failure for individual series, and is not a direct option for handling errors in 'TRY_COMPLETE in this manner; 'TRY_COMPLETE itself returns NULL on error.


NEW QUESTION # 258
A Gen AI specialist is designing a RAG pipeline utilizing Cortex Search for an application that queries a large repository of unstructured text documents. To optimize the quality of retrieval and subsequent LLM responses, what are the critical best practices and understanding of Cortex Search's mechanisms that the specialist should consider regarding text processing and tokenization?

Answer: B,E

Explanation:
Option A is correct; Snowflake recommends splitting text into chunks of no more than 512 tokens for optimal search results and higher retrieval/LLM response quality, even with longer-context embedding models. Option B is incorrect because while Cortex Search truncates strings exceeding the context window for embedding, it uses the full body of text for keyword-based retrieval. Option C is incorrect as research shows smaller chunk sizes typically result in higher retrieval and downstream LLM response quality, despite the availability of longer-context models. Option D is correct, as COUNT _ TOKENS is a helper function used for this purpose. Option E is incorrect because Cortex Search is a hybrid (vector and keyword) search engine.


NEW QUESTION # 259
A data scientist needs to fine-tune a 'mistral-7b' LLM using Snowflake Cortex for a specific text summarization task. They have prepared a training dataset in a Snowflake table, with text in a column named 'source_text' and the desired summaries in a column named 'expected_summary' . They also want to understand the cost implications. Which SQL statement will correctly initiate the fine-tuning job, and how will the cost be primarily calculated?

Answer: E

Explanation:
Option B is correct. The 'SNOWFLAKE.CORTEX.FINETUNE function requires the training data query result to include columns named 'prompt' and 'completion'. Using SELECT ... AS prompt, ... AS completion" aliases the existing columns to the required names. The cost for fine-tuning is based on the number of tokens used in training, specifically calculated as 'Fine-tuning trained tokens = number of input tokens ' number of epochs trainecf. Additionally, running 'AI_COMPLETE on a fine-tuned model incurs compute costs for both input and output tokens processed. Option A is incorrect because the columns need to be aliased to 'prompt' and 'completion', and it misstates the cost calculation for fine-tuning training itself. Option C is incorrect because the syntax for FINETUNE is a SQL function, not a 'CREATE SNOWFLAKE.ML.FINETUNE' command, and Cortex Fine-tuning incurs compute costs based on tokens, not a fixed-rate subscription model. Option D is incorrect because the first argument for the function is 'CREATE', not 'TRAIN', and training costs are based on tokens, not GPU compute hours, although compute resources are utilized. Option E is incorrect because the training data must come from a Snowflake table or view, and costs are incurred for training and inference, not just storage.


NEW QUESTION # 260
A data engineering team is designing a scalable data pipeline in Snowflake that involves processing large text inputs with Cortex AI LLM functions. They want to ensure cost efficiency and prevent queries from failing due to exceeding LLM context window limits. They plan to use SNOWFLAKE. CORTEX. COUNT_TOKENS for pre-validation. Which of the following statements are TRUE about the role and cost of COUNT_TOKENS in this scenario? (Select all that apply)

Answer: B,D,E

Explanation:
Option A is correct. Embedding models like

have a fixed context window of 512 tokens. Using COUNT_TOKENS allows pre-checking if text fits within this limit, preventing truncation that can occur when the input exceeds the context window. Option B is incorrect because COUNT_TOKENS incurs only compute cost to run the function and does not incur additional token-based costs that would add to the billing of subsequent LLM inference calls. Option C is correct. For best search results, Snowflake recommends splitting text into chunks of no more than 512 tokens. This practice generally leads to higher retrieval and downstream LLM response quality in RAG scenarios, and COUNT_TOKENS is a valuable tool for managing these chunk sizes. Option D is incorrect. While COUNT_TOKENS incurs compute cost (not token-based cost), the amount of compute would generally scale with the size of the input text it processes, making the cost not entirely independent of input length, although it's not billed on a per-token basis for its own operation. Option E is correct. The COUNT_TOKENS function is available in all regions for any model, though the models themselves may have specific regional availabilities for other functions.


NEW QUESTION # 261
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