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

解説: (Fast2test メンバーにのみ表示されます)

A development team is building an AI-powered data pipeline in Snowflake. The pipeline involves extracting text from documents, generating embeddings using

,and then performing similarity searches using

to find related documents. They plan to manage this pipeline using Snowflake tasks and want to integrate with a Python application for some custom processing. Considering this scenario, which of the following statements about implementing this pipeline are true?

• A.
  
• B. Snowflake
• C. If the team wants to use the Snowpark Python library to call
  
• D. When using Snowflake tasks to automate the embedding generation and similarity search,
• E. To generate document embeddings, the

解説: (Fast2test メンバーにのみ表示されます)

An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch' , 'transformers' , 'opencv-python'). The scenario dictates that 'opencv-python' is only available via PyPl, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPl. The engineer uses the Snowflake Model Registry to log the model. Which of the following and configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

解説: (Fast2test メンバーにのみ表示されます)

A financial data team is implementing a Snowflake Cortex AI solution to summarize regulatory documents using SNOWFLAKE.CORTEX.TRY_COMPLETE They aim for both cost efficiency and high reliability, especially when dealing with documents that might occasionally exceed model context limits or result in malformed output. Which of the following statements about the cost and operational behavior of TRY_COMPLETE are TRUE in this context? (Select all that apply)

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

解説: (Fast2test メンバーにのみ表示されます)

A Gen AI developer has a Document AI pipeline that uses a query with 'GET PRESIGNED URL' to process multi-page PDF documents. Despite the internal stage being correctly set up with 'SNOWFLAKE SSE' encryption and the model build being published, they observe inconsistent results. Some documents result in a Received HTTP 403 response for presigned URL. URL may be expired.
error, while other documents (containing complex diagrams and dense text in an unsupported language like Korean) are processed, but the extracted information is often incomplete or inaccurate.
Which two factors are most likely contributing to these observed issues?

• A. The default expiration time for the 'GET PRESIGNED URL' function is causing some URLs to expire before the Document AI model can process them.
• B. The role lacks the 'EXECUTE TASK' privilege, preventing the scheduled pipeline tasks from running.
• C. The '!PREDICT method is being called with an outdated model build version instead of the latest one, leading to performance degradation.
• D. The Document AI model is returning answers longer than its limit of 512 tokens for entity extraction or 2048 tokens for table extraction.
• E. The documents are in an unsupported language or exceed the maximum page length of 125 pages, causing extraction failures or inaccuracies.

解説: (Fast2test メンバーにのみ表示されます)