GES-C01 무료 덤프문제 온라인 액세스

A development team is building a RAG application in Snowflake Cortex that needs to extract high-fidelity text and layout from a collection of technical documentation PDFs stored in an internal stage to power semantic search and LLM responses. They want to ensure proper context retrieval for complex user queries. Given this scenario, which of the following actions or statements are crucial for effectively leveraging AI_PARSE_DOCUMENT to optimize the RAG pipeline?

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

An ML Engineer has developed a custom PyTorch model for GPU-powered inference and successfully built an OCI-compliant image locally. They now need to push this image to a Snowflake image repository and configure a Snowpark Container Service to use it. The Snowflake account identifier is my org_name_my_account_id_prod. Which set of commands correctly demonstrates tagging the local image and pushing it to the repository?

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

A financial institution is fine-tuning a llama3.1-70b model within Snowflake Cortex using sensitive internal financial reports to improve sentiment analysis on earnings call transcripts. They need to understand the implications for data privacy, model ownership, and how this fine-tuned model can be managed and shared. Which of the following statements are true regarding this process?

• A.The fine-tuning process requires the explicit provisioning and management of a Snowpark-optimized warehouse with GPU resources by the institution.
• B.The fine-tuned model, being of type CORTEX_FINETUNED, can be shared with other Snowflake accounts using secure data sharing capabilities.
• C.Fine-tuned LLMs built with Cortex Fine-tuning are fully managed through the Snowflake Model Registry API, allowing for programmatic deployment, version control, and comprehensive lifecycle management.
• D.The financial reports used for fine-tuning the llama3.1-7b model are securely isolated and are not used by Snowflake to train or re-train models for other customers.
• E.The resulting fine-tuned model (e.g., my_sentiment_model) is the exclusive property of the financial institution and cannot be accessed or used by any other Snowflake customer.

A machine learning engineer needs to fine-tune the 'mistral-7b' LLM using Snowflake Cortex for a specialized task. They have prepared training data in a Snowflake table. Which of the following statements correctly describe the process, requirements, and cost considerations for initiating this fine-tuning job?

• A.The training data query result must explicitly include columns named
• B.The fine-tuned model will automatically appear in the Snowflake Model Registry and is available for sharing with other accounts using Data Sharing, even if it contains user code.
• C.To create the fine-tuning job, the engineer should use the SQL command:
  
• D.The 'max_epochs' option in the 'options' JSON object can be set to any positive integer to control the training duration, but it is capped at a maximum of 50 epochs.
• E.The cost for fine-tuning is incurred based on the number of tokens used in training, calculated as:
  

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?

• A.The fine-tuning job is initiated by providing the prompt and completion data directly as arrays within the 'FINETUNE' function call, avoiding the need for a separate training data table, and costs are only for the storage of the fine-tuned model.
• B.The fine-tuning job is initiated by:
  
• C.The fine-tuning job is initiated by:
  
• D.The fine-tuning job is initiated by:
  
• E.The fine-tuning job is initiated by: