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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
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 enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.
B. It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.
C. It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
D. It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
E. 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.

Answer: A,B

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 # 52
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 C
B. Option E
C. Option B
D. Option A
E. Option D

Answer: C

Explanation:

NEW QUESTION # 53
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?

A. To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
B. Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
C. For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like
D. The
E. Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.

Answer: B,C,D

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 # 54
A development team is creating a new search application using Snowflake Cortex Search. They are currently using a 'snowflake-arctic- embed-I-v2.0' embedding model. After an initial load of 10 million rows, each with approximately 500 tokens of text, they observe a significant 'EMBED_TEXT_TOKENS' cost. They want to minimize these costs for future updates and ongoing operations. Considering their goal to optimize 'EMBED_TEXT_TOKENS' costs, which two strategies should the team prioritize for their Cortex Search Service?

A. Option E
B. Option A
C. Option D
D. Option B
E. Option C

Answer: B,E

Explanation:
Option A is correct: Different embedding models have varying costs per million tokens. Switching from 'snowflake-arctic-embed-l- v2.0' (0.05 credits/M tokens) to 'snowflake-arctic-embed-m-v1 .5' (0.03 credits/M tokens) would directly reduce costs if the smaller model meets quality requirements. Option C is correct: The parameter controls how often the search service is refreshed. Increasing the 'TARGET_LAG' reduces the frequency of embedding jobs, directly decreasing 'EMBED_TEXT_TOKENS costs over, time. Option B is incorrect: 'EMBED_TEXT_TOKENS' costs are based on the total number of tokens processed. Splitting a 500-token row into smaller chunks still results in processing the same total number of tokens for that row, so it doesn't reduce the total 'EMBED_TEXT _ TOKENS' cost, although it can improve search quality. Option D is incorrect: costs are based on the volume of tokens, not processing speed. A larger warehouse size does not reduce the number of tokens and is not recommended for cost reduction for EMBED_TEXT_TOKENS'; Snowflake recommends a warehouse size no larger than MEDIUM for Cortex Search services. Option E is incorrect: The 'ATTRIBUTES' field primarily affects filtering capabilities, and the embedding cost is associated with the primary search column, not each individual attribute incurring a separate embedding cost.

NEW QUESTION # 55
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?

A. Option C
B. Option E
C. Option B
D. Option A
E. Option D

Answer: C

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 # 56
......

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