GES-C01 試験問題を無料オンラインアクセス

An analytics engineering team is building a complex, real-time data pipeline in Snowflake. They want to automatically summarize new incoming product reviews using SNOWFLAKE. CORTEX. SUMMARIZE as part of a continuous process. They consider integrating this function into a dynamic table definition for efficient, automated refreshes. Which of the following statements regarding the integration of SNOWFLAKE. CORTEX. SUMMARIZE with Snowflake's data pipeline features is true?

• A.SNOWFLAKE. CORTEX. SUMMARIZE can be used with dynamic tables if the TARGET_LAG is explicitly set to '1 day' or longer, allowing for asynchronous processing.
• B.SNOWFLAK CORTEX. SUMMARIZE is supported in dynamic tables only if a Snowpark-optimized warehouse is explicitly assigned to the dynamic table for processing.
• C.Snowflake Cortex functions, including SNOWFLAKE. CORTEX.SUMMARIZE, are explicitly incompatible with dynamic tables, requiring alternative automation methods such as streams and tasks.
• D.While SNOWFLAKE. CORTEX. SUMMARIZE does not support dynamic tables, its updated version, is fully compatible with dynamic tables for continuous summarization.
• E.SNOWFLAKE. CORTEX. SUMMARIZE can be directly used within a dynamic table's SELECT statement, enabling real-time summarization with automated refreshes, provided the source data is a stream.

A Gen AI Specialist is tasked with enhancing a Cortex Analyst semantic model to improve the accuracy of literal string searches for product names within user queries. The product names are stored in a high-cardinality PRODUCT_NAME column in the underlying PRODUCT table. The current semantic model already defines a dimension for PRODUCT_NAME. Which of the following configurations and considerations are essential for integrating Cortex Search with Cortex Analyst to achieve this goal?

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

A data science team is fine-tuning a Snowflake Document AI model to improve the extraction accuracy of specific fields from a new type of complex legal document. They are consistently observing low confidence scores and inconsistent 'value' keys for extracted entities, even after initial training. Which two of the following best practices should the team follow to most effectively improve the model's extraction accuracy and confidence for this complex document type?

• A.Ensure the training dataset used for fine-tuning includes diverse documents representing various layouts, data variations, and explicit examples of 'NULL' values or empty cells where appropriate.
• B.Prioritize extensive prompt engineering by creating highly detailed and complex questions with intricate logic to guide the LLM's understanding of the extraction task.
• C.Actively involve subject matter experts (SMEs) or document owners throughout the iterative process to help define data values, provide annotations, and evaluate the model's effectiveness.
• D.Limit the fine-tuning training data exclusively to perfectly formatted and clean documents to ensure the model learns from ideal examples without noise.
• E.Set the 'temperature' parameter to a higher value (e.g., 0.7) during '!PREDICT calls to encourage more creative and diverse interpretations by the model.

A team of data application developers is leveraging Snowflake Copilot to streamline the creation of analytical SQL queries within their Streamlit in Snowflake application. They observe that Copilot sometimes struggles with complex joins or provides suboptimal queries when dealing with a newly integrated, deeply nested dataset. Based on Snowflake's best practices and known limitations, which actions or considerations would help improve Copilot's performance in this scenario?

• A.Break down complex requests into simpler, multi-turn questions, as Copilot is designed to build complex queries through conversational refinement and follow-up questions.
• B.Ensure that a database and schema are explicitly selected for the current session, and that column names are meaningful, to provide Copilot with better context for query generation.
• C.Grant Copilot direct access to the raw data using ACCOUNTADMIN privileges, allowing it to infer schema relationships more effectively from data content.
• D.Implement curated views with descriptive and easy-to-understand names for views and columns, appropriate data types, and pre-define common/complex joins to simplify the underlying schema for Copilot.
• E.Enable the CORTEX_MODELS_ALLOWLIST parameter to restrict Copilot to only use the largest available LLMs, thereby guaranteeing higher accuracy for complex queries.

A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?

• A.Scale down the virtual warehouse to 'X-SMALC or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
• B.Increase the 'max_tokenS parameter within the ' !PREDICT' function options to accommodate longer document responses from the model.
• C.Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
• D.Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE'Y.
• E.Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.