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[General] New Snowflake GES-C01 Braindumps - GES-C01 Exams Collection

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【General】 New Snowflake GES-C01 Braindumps - GES-C01 Exams Collection

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q117-Q122):NEW QUESTION # 117
A Snowflake administrator is designing a new role, 'doc_ai_pipeline_creator' , intended to configure and deploy Document AI extraction pipelines. This role needs the ability to ensure that the designated virtual warehouse, 'analytics_wh', can be reliably started and stopped as needed for Document AI tasks. Which of the following SQL statements grants the privilege that directly enables the 'doc ai_pipeline_creator' role to control the operational state of 'analytics_wh'?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: C
Explanation:
For Document AI operations, including setting up model builds and running extraction pipelines, the associated virtual warehouse must be available and its operational state managed effectively. The 'OPERATE' privilege on a virtual warehouse specifically grants the ability to start and stop (resume and suspend) the warehouse. - Option A USAGE ON WAREHOUSE) allows a role to select and use a warehouse but does not provide control over its active state. Both 'USAGE* and *OPERATE* are required for Document AI. - Options B, D, and E CMONITOR , MODIFY , "CONTROL") are not the specific privilege described in the sources for controlling the operational state of a warehouse in the context of Document AI setup.

NEW QUESTION # 118
A data engineering team is designing a Snowflake data pipeline to automatically enrich a 'customer issues' table with product names extracted from raw text-based 'issue_description' columns. They want to use a Snowflake Cortex function for this extraction and integrate it into a stream and task-based pipeline. Given the 'customer_issues' table with an 'issue_id' and (VARCHAR), which of the following SQL snippets correctly demonstrates the use of a Snowflake Cortex function for this data enrichment within a task, assuming is a stream on the 'customer issues' table?

  • A. Option C
  • B. Option B
  • C. Option D
  • D. Option A
  • E. Option E
Answer: B
Explanation:
Option B correctly uses to pull specific information (product name) from unstructured text, which is a common data enrichment task. It also integrates with a stream ('issue_stream') by filtering for 'METADATA$ACTION = 'INSERT" and uses a 'MERGE statement, which is suitable for incremental updates in a data pipeline by inserting new extracted data based on new records in the stream. Option A uses for generating a response, not for specific entity extraction, and its prompt is less precise for this task than 'EXTRACT_ANSWER. Option C uses 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT for classification, not direct entity extraction of a product name, and attempts to update the source table directly, which is not ideal for adding new columns based on stream data. Option D proposes a stored procedure and task, which is a valid pipeline structure. However, the EXTRACT ANSWER call within the procedure only returns a result set and does not demonstrate the final insertion or merging step required to persist the extracted data into an 'enriched_issues' table. Option E uses to generate vector embeddings, which is a form of data enrichment, but the scenario specifically asks for 'product names' (a string value), not embeddings for similarity search.

NEW QUESTION # 119

  • A. The 'ACCOUNTADMIN' must execute the SQL command:
  • B. If a user attempts to call an LLM not explicitly listed in the via the Cortex LLM Playground, Snowflake will automatically fall back to the 'snowflake-arctic' model for that request.
  • C. Users with 'ds_team_role' will still be able to successfully call
  • D. Enabling Cortex Guard for 'AI_COMPLETE through the 'guardrails' option automatically bypasses the 'CORTEX_MODELS_ALLOWLIST to ensure all LLMs can be used for content safety filtering.
  • E. The parameter provides granular control at the database or schema level, allowing administrators to define different sets of approved models for different data contexts.
Answer: A,C
Explanation:


NEW QUESTION # 120
A data engineer is working with Snowflake Cortex Analyst to improve its ability to answer natural language questions by precisely identifying product names for filtering. They have decided to integrate a Cortex Search Service with their semantic model to enhance literal search for the 'product_name' dimension. Which of the following configurations within the semantic model's YAML file are valid and effective for this purpose?
  • A. Setting true' for the 'product_name' dimension and providing an exhaustive list of to restrict the model to only those values.
  • B. Including in the semantic model's 'metrics' section, referencing 'product_name'.
  • C. Only specifying 'sample_valueS for the 'product_name' dimension without a entry.
  • D. Adding a 'cortex_search_service' entry to the 'product_name' dimension with only the 'service' field:
  • E. Adding a entry to the 'product_name' dimension, including 'literal_column' and ensuring the search service is configured to index the physical column:

Answer: A,E
Explanation:
Option C is correct because integrating a Cortex Search Service with Cortex Analyst for literal search improvement involves adding a entry to the relevant dimension. Specifying the 'literal_column' explicitly helps map the semantic dimension to the underlying column indexed by the search service, ensuring the correct values are used for semantic search. Option D is also correct. For dimensions with relatively low-cardinality values, setting 'is_enum: true and providing 'sample_values' tells the model to choose only from that predefined list, which is an effective way to improve literal usage without requiring an external Cortex Search Service. Option A is incorrect because while 'sample_valueS are used for semantic similarity search for low-cardinality dimensions, they don't leverage the full capabilities of a Cortex Search Service for 'fuzzy' search over potentially high-cardinality data. Option B is incomplete. While specifying the 'service' name is a start, it's beneficial to explicitly define the 'literal_column' if it differs from the dimension's expression or if precision is critical. Option E is incorrect because is a configuration for a 'dimension', not a 'metric'.

NEW QUESTION # 121
A legal department uses Snowflake to manage and review large volumes of contracts. They need to automate the process of finding specific pieces of information, such as the effective_date or involved_parties, from these unstructured contract texts. They are considering using SNOWFLAKE. CORTEX. EXTRACT_ANSWER. Which characteristic correctly describes the primary intent or behavior of SNOWFLAKE. CORTEX. EXTRACT_ANSWER, distinguishing it from other LLM functions?
  • A. It evaluates a text and returns a numeric score indicating the overall positive or negative sentiment.
  • B. It focuses on identifying and returning a direct answer to a specific question contained within the provided source document.
  • C. It classifies an input text into one of several predefined categories provided by the user.
  • D. It transforms an input document into a summarized version, reducing its length while preserving key information.
  • E. It is primarily designed to generate entirely new text based on a given prompt, much like a conversational AI.
Answer: B
Explanation:
Option D is correct. The 'SNOWFLAKE.CORTEX.EXTRACT_ANSWER function is specifically designed to extract an answer to a given question from a text document. Option A describes the 'COMPLETE function. Option B describes the 'SENTIMENT function. Option C describes the 'CLASSIFY_TEXT function. Option E describes the 'SUMMARIZE function.

NEW QUESTION # 122
......
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