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[General] GES-C01 Exam Collection, GES-C01 Test Discount Voucher

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【General】 GES-C01 Exam Collection, GES-C01 Test Discount Voucher

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q62-Q67):NEW QUESTION # 62
A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline?

  • A. Option C
  • B. Option D
  • C. Option E
  • D. Option A
  • E. Option B
Answer: A
Explanation:
To ensure that LLM responses adhere to a predefined JSON structure, the 'AI_COMPLETE function's 'response_format' argument, which accepts a JSON schema, is the most effective and direct method. This mechanism enforces the structure, data types, and required fields, significantly reducing the need for post-processing and ensuring deterministic, high-quality output. The AI-Infused Data Pipelines with Snowflake Cortex blog highlights asking the LLM to create a JSON object for maximizing utility. While setting 'temperature' to 0 can improve consistency, it does not enforce a specific schema. Prompt engineering (Option A) can help but does not guarantee strict adherence. Using multiple extraction calls (Option D) is less efficient and robust for extracting multiple related fields than a single 'AI_COMPLETE call with a structured output schema. Snowflake Cortex does not automatically infer and enforce a JSON schema without explicit configuration (Option E).

NEW QUESTION # 63
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. The
  • C. For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
  • D. Using the
  • E. To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
Answer: D,E
Explanation:


NEW QUESTION # 64
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 full text of the natural language questions submitted by the users.
  • B. The complete request and response bodies associated with the application's execution steps.
  • C. Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
  • D. The exact SQL queries that Cortex Analyst generated in response to user questions.
  • E. Any error messages or warnings that occurred during the processing of the request.
Answer: A,B,D,E
Explanation:
Cortex Analyst logs requests to an event table to aid in refining semantic models or views. These logs are comprehensive and include specific details crucial for debugging and monitoring. The captured information includes 'The user who asked the question', 'The question asked', 'Generated SQL', 'Errors and/or warnings', 'Request and response bodies', and 'Other metadata'. Therefore, options A, B, C, and D are all accurate descriptions of the data available in these event logs. Option E, real-time CPU and memory usage, refers to infrastructure monitoring metrics rather than the content specifically logged within the application's execution event table by Cortex Analyst itself.

NEW QUESTION # 65
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 C
  • B. Option D
  • C. Option E
  • D. Option A
  • E. Option B
Answer: A
Explanation:
Option C is the most direct and efficient approach for continuously augmenting data with sentiment scores in a Snowflake pipeline. is a task-specific AI function designed for this purpose, returning an overall sentiment score for English-language text. SNOWF LAKE .CORTEX.SENTIMENT Integrating it directly into a task that monitors a stream allows for automated, incremental processing of new data as it arrives in the stage. The source explicitly mentions using Cortex functions in data pipelines via the SQL interface. Option A is plausible, but calling SENTIMENT directly in SQL within a task (Option C) is simpler and avoids the overhead of a Python UDF if the function is directly available in SQL, which it is. Option B, using a dynamic table, is not supported for Snowflake Cortex functions. Option D, while powerful for custom LLMs, is an over-engineered solution and introduces more complexity (SPCS setup, custom service) than necessary for a direct sentiment function. Option E describes a manual, non- continuous process, which contradicts the requirement for an automated pipeline.

NEW QUESTION # 66
A security administrator needs to manage access to Snowflake Cortex LLM functions and models, and ensure compliant usage across different regions for various teams. Which configuration options and parameters are relevant for these requirements?

  • A. Option C
  • B. Option D
  • C. Option E
  • D. Option A
  • E. Option B
Answer: A,D,E
Explanation:


NEW QUESTION # 67
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