GES-C01 무료 덤프문제 온라인 액세스

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.The function should be called using 'TRY_COMPLETE instead of directly to handle potential errors gracefully and avoid incurring costs for failed operations.
• B.For 'EMBED_TEXT 1024' , billing is based on both input and output tokens, encouraging brevity in generated embeddings to control costs.
• C.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.
• D.To minimize compute costs, the team should use a Snowpark-optimized warehouse for operations, as it is specifically designed for ML workloads.
• E.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.

A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role 'DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet' model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements?

• A.
• B.
• C.
• D.
• E.

An ML engineer is planning a fine-tuning project for a
llama3.1-8b
model to summarize long customer support tickets. They are considering the impact of dataset size and max_epochs on cost and performance, as well as the behavior of the fine-tuned model for inference. Which statements about cost and performance in Snowflake Cortex Fine-tuning are true? (Select all that apply)

• A.The compute cost for fine-tuning is primarily determined by multiplying the number of input tokens in the training data by the number of epochs trained.
• B.D When fine-tuning a
• C.For optimal cost efficiency, especially with smaller datasets, the
• D.The cost for inferencing with a fine-tuned model using the
• E.For large fine-tuning jobs with substantial datasets, particularly when exceeding millions of rows, utilizing Snowpark-optimized warehouses is recommended for improved performance during the training phase.

A Gen AI Specialist is tasked with implementing a data pipeline to automatically enrich new customer feedback entries with sentiment scores using Snowflake Cortex functions. The new feedback arrives in a staging table, and the enrichment process must be automated and cost-effective. Given the following pipeline components, which combination of steps is most appropriate for setting up this continuous data augmentation process?

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

A data scientist wants to fine-tune a
mistral -7b
model to improve its ability to generate specific product descriptions based on brief input features. They have a table named PRODUCT_CATALOG with columns PRODUCT_FEATURES (text) and GENERATED_DESCRIPTION (text). Which of the following statements correctly describe the preparation and initiation of this fine-tuning job in Snowflake Cortex?
(Select all that apply)

• A.Once a fine-tuned model is created, it is fully managed by the Snowflake Model Registry API, allowing for programmatic updates to its parameters and versions.
• B.The SQL query for the training data must select columns aliased as
• C.O To generate highly structured
• D.The
• E.The fine-tuning job must be created using a