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GES-C01 100% Exam Coverage, GES-C01 Fresh Dumps

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GES-C01 100% Exam Coverage, GES-C01 Fresh Dumps

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P.S. Free 2026 Snowflake GES-C01 dumps are available on Google Drive shared by PDFDumps: https://drive.google.com/open?id=148FQv_YJm51cwkRLApslpZpt1S26WdqM
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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q245-Q250):NEW QUESTION # 245
An AI developer is testing a new RAG application in Snowflake. The application uses

in this scenario?

  • A. Option A
  • B. Option B
  • C. Option E
  • D. Option C
  • E. Option D
Answer: A,B
Explanation:
Option A is correct. If access to a specific model is restricted by the

Option C is incorrect; setting 'temperature' to 0 makes the output more deterministic and focused, which generally improves adherence to instructions and is recommended for consistent results, not causes failures. Option D is incorrect. The

, with smaller warehouses (no larger than MEDIUM), meaning an X-Small warehouse is generally sufficient for executing the function itself, though it might impact performance for very large datasets if used for other operations.

NEW QUESTION # 246
A data analytics team is building a self-service analytics application using Snowflake Cortex Analyst to allow business users to query sales data with natural language. They are defining a semantic model in YAML to ensure accurate text-to-SQL generation. Which of the following is the most crucial aspect of the semantic model's configuration for Cortex Analyst to effectively translate natural language into SQL for structured data?
  • A. Providing detailed 'name', 'description' , and 'synonyms' for logical tables, dimensions, and facts to bridge the gap between business terminology and the underlying database schema.
  • B. Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.
  • C. Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.
  • D. Defining a comprehensive 'verified_queries' section with a high volume of example natural language questions and their exact SQL translations to handle all potential user queries.
  • E. Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.
Answer: A
Explanation:
Option C is correct because the primary purpose of a semantic model in Cortex Analyst is to provide semantic information about your data, bridging the gap between business users' natural language and the technical database schema. This includes using descriptive names, synonyms, and descriptions for logical tables, dimensions, and facts, which is essential for Cortex Analyst to reliably generate accurate SQL from natural language questions. Option A is incorrect; while Cortex Search Services can improve literal matching for dimensions, it's an enhancement and not the most crucial foundational aspect of the semantic model for general text-to-SQL translation, nor is it required for 'every' dimension. Option B is incorrect because while 'verified_queries' improve accuracy for similar questions, a high volume of examples for 'all' potential queries is not feasible or the most crucial initial configuration; the core mapping (Option C) is more fundamental. Option D is incorrect as the 'base_table' must refer to a physical table or view, not directly to a dynamic table. Furthermore, Cortex functions do not support dynamic tables directly. Option E is incorrect because 'VARIANT, 'OBJECT, 'GEOGRAPHY , and 'ARRAY data types are explicitly not supported for dimension, fact, or metric columns in a semantic model.

NEW QUESTION # 247
A Gen AI specialist is designing a RAG pipeline utilizing Cortex Search for an application that queries a large repository of unstructured text documents. To optimize the quality of retrieval and subsequent LLM responses, what are the critical best practices and understanding of Cortex Search's mechanisms that the specialist should consider regarding text processing and tokenization?
  • A. Cortex Search operates solely on vector embeddings for semantic search; keyword-based retrieval is handled by a separate, less efficient mechanism outside the core search service.
  • B. Embedding models with larger context windows, such as snowflake-arctic-embed-1-v2.e-8k (8000 tokens), are always superior as they allow the RAG system to process the entire document as a single, highly relevant chunk.
  • C. For best search results, text in the search column should be split into chunks of no more than 512 tokens, as smaller chunks generally lead to more precise retrieval and relevant LLM context.
  • D. When text input exceeds an embedding model's context window, Cortex Search truncates the text for both semantic embedding and keyword-based retrieval, potentially losing critical information.
  • E. The SNOWFLAKE .CORTEX. COUNT TOKENS function is a helper function that can be used to accurately determine the token count for a given string based on a specified model, aiding in adherence to context window limits.
Answer: C,E
Explanation:
Option A is correct; Snowflake recommends splitting text into chunks of no more than 512 tokens for optimal search results and higher retrieval/LLM response quality, even with longer-context embedding models. Option B is incorrect because while Cortex Search truncates strings exceeding the context window for embedding, it uses the full body of text for keyword-based retrieval. Option C is incorrect as research shows smaller chunk sizes typically result in higher retrieval and downstream LLM response quality, despite the availability of longer-context models. Option D is correct, as COUNT _ TOKENS is a helper function used for this purpose. Option E is incorrect because Cortex Search is a hybrid (vector and keyword) search engine.

NEW QUESTION # 248
An operations manager is tasked with monitoring the cost and ensuring compliance for a Cortex Analyst deployment that uses the REST API. They are particularly concerned with accurately tracking credit consumption and understanding the implications of enabling external models. Which of the following statements correctly describe aspects of Cortex Analyst cost and governance?
  • A. Cortex Analyst credit usage is based on the number of messages processed, at a rate of 67 Credits per 1 ,000 messages, and only successful responses (HTTP 200) are counted.
  • B. Enabling Azure OpenAI models via the ' ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAF parameter ensures that all customer data and prompts remain within Snowflake's governance boundary and fully respect RBAC policies for those models.
  • C. Credit consumption for Cortex Analyst is primarily based on the number of tokens processed by the underlying LLMs, with more complex natural language questions leading to higher token usage and costs.
  • D. The view can be queried to track detailed information about cortex Analyst requests, including generated SQL and any errors.
  • E. Snowflake's view provides granular usage information for REST API requests to cortex Analyst, including tokens processed per model.
Answer: A
Explanation:


NEW QUESTION # 249
A data engineer is developing a Snowflake Cortex LLM application that processes sensitive customer feedback. To ensure that generated responses from the 'COMPLETE' function are filtered for potentially unsafe or harmful content, they need to enable Cortex Guard. Which of the following SQL 'COMPLETE function calls correctly demonstrates the enablement of Cortex Guard with the default unsafe response message?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: C
Explanation:
To enable Cortex Guard within the 'COMPLETE function, the 'guardrails' argument must be set to 'TRUE within the options object. When is not explicitly specified, the default message 'Response filtered by Cortex Guard' is returned for unsafe content. Option C correctly uses 'guardrails: TRUE without specifying 'response_when_unsafe', thus implying the default behavior Option B includes , which is not the default. Options A, D, and E use incorrect or non-existent arguments for enabling guardrails.

NEW QUESTION # 250
......
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