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Snowflake SnowPro® Specialty: Gen AI Certification 認定 GES-C01 試験問題:

1. An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?

A) The exact SQL queries that Cortex Analyst generated in response to user questions.
B) The full text of the natural language questions submitted by the users.
C) Any error messages or warnings that occurred during the processing of the request.
D) Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
E) The complete request and response bodies associated with the application's execution steps.

2. A Gen AI developer is implementing a Cortex Search Service for a RAG application and needs to configure the text splitting for optimal performance using SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER Which of the following statements represent best practices or outcomes when applying text splitting with this function for Cortex Search in a RAG scenario? (Select all that apply)

A) The function automatically enriches each text chunk with relevant metadata about its original document, such as author and creation date, for enhanced filtering capabilities in Cortex Search.
B) Smaller chunk sizes generally lead to higher retrieval precision for a given query in a RAG system.
C) Even when using embedding models with larger context windows (e.g., 8000 tokens), a smaller chunk size is typically preferred for improved retrieval and downstream LLM response quality.
D) Optimal text splitting using this function ensures that the number of input tokens precisely equals the number of output tokens for subsequent LLM calls, thereby minimizing compute costs.
E) Snowflake recommends splitting text into chunks of no more than 512 tokens for best search results in Cortex Search.

3. A security auditor needs to access and analyze logs generated by Snowflake AI Observability for compliance auditing and to track the activity of generative AI applications. They need to understand how to reliably query this data and its temporal characteristics within Snowflake. Which of the following statements accurately describes the access and characteristics of this logged data?

A) Logged data from AI Observability's event tables becomes visible within a small latency, typically 1-2 minutes, after a request is made.
B) Detailed request and response bodies, along with the generated SQL, are stored and can be directly queried using standard SQL.
C) The logs are automatically purged after 7 days of being recorded, requiring a separate process for long-term data retention.
D) Logs are exclusively available for analysis through pre-built dashboards in Snowsight and cannot be accessed via direct SQL queries.
E) Access to these detailed event tables is implicitly granted to roles holding the SNOWFLAKE. CORTEX_USER database role and the AI_OBSERVABILITY_EVENTS_LOOKUP application role.

4. A data architect is integrating Snowflake Cortex LLM functions into various data enrichment pipelines. To ensure optimal performance, cost-efficiency, and accuracy, which of the following are valid best practices or considerations for these pipelines?

A) When extracting specific entities from documents using SAI EXTRACT or '!PREDICT , it is often more effective to fine-tune a Document AI model for complex or varied document layouts rather than relying solely on extensive prompt engineering for zero-shot extraction.
B) For data enrichment involving classification with 'AI_CLASSIFY' , using descriptive and mutually exclusive categories in plain English, along with an optional clear task description, can significantly improve classification accuracy.
C) To manage costs effectively for LLM functions like SAI COMPLETE in a pipeline, always use the largest available warehouse size (e.g., 6XL Snowpark- optimized) to maximize throughput, as this directly reduces the overall token processing time and cost.
D) When performing sentiment analysis on customer feedback using 'AI_SENTIMENT, it's best practice to pass detailed, multi-turn conversation history to the function to enhance accuracy, similar to how 'AI_COMPLETE handles conversational context.
E) For tasks requiring deterministic JSON outputs, explicitly specifying a JSON schema using the 'response_format' argument with 'AI COMPLETE is crucial, and for OpenAI (GPT) models, including the 'required' field and setting 'additionalPropertieS to 'false' in every node of the schema is a mandatory requirement.

5. A data engineering manager needs to audit Cortex LLM function costs to identify specific SQL queries that are unexpectedly high in token consumption for the 'llama3.1-8b' model. They require granular analysis of prompt, completion, and guardrail token usage for these queries. Which of the following Snowflake methods or views would provide the necessary insights?

A) Option A
B) Option C
C) Option E
D) Option D
E) Option B

質問と回答：