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[Hardware] Quiz Valid Snowflake - GES-C01 - SnowPro® Specialty: Gen AI Certification E

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【Hardware】 Quiz Valid Snowflake - GES-C01 - SnowPro® Specialty: Gen AI Certification E

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q271-Q276):NEW QUESTION # 271
A data analyst needs to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to streamline information retrieval from various contract documents. They are new to Cortex functions and want to understand access requirements and optimal usage. Which of the following statements about using SNOWFLAKE .CORTEX. EXTRACT_ANSWER are correct?
  • A. If EXTRACT_ANSWER encounters an unresolvable issue during processing, it returns NULL instead of an error, similar to TRY_COMPLETE.
  • B. It is generally recommended to process multiple documents by applying EXTRACT_ANSWER to a table column containing the document texts, allowing for efficient batch processing.
  • C. EXTRACT_ANSWER is the most current and recommended function for all text extraction tasks, offering multi-label and image extraction capabilities.
  • D. For optimal accuracy, the source_document input should be in plain English, and the question should be specific, asking for a single value.
  • E. The analyst's role must be granted the SNOWFLAKE. CORTEX_USER database role to execute EXTRACT_ANSWER functions.
Answer: B,D,E
Explanation:
Option A is correct because users must use a role that has been granted the 'SNOWFLAKE.CORTEX USER database role to access EXTRACT_ANSWER and other Cortex AI functions. Option B is correct because for optimal performance and accurate responses, it is recommended to use plain English text for input and categories, and questions should be specific and ask for a single value. Option C is incorrect; EXTRACT_ANSWER would typically raise an error if an operation cannot be performed. The 'TRY_COMPLETE function is specifically designed to return 'NULL' instead of an error in such cases. Option D is incorrect; 'EXTRACT ANSWER is an older version of this function, and 'AI_EXTRACT' is the latest version, which supports additional capabilities like image and multi-language extraction. Option E is correct; EXTRACT ANSWER can be called on a table column, enabling efficient batch processing of multiple documents or text entries. This is a common pattern for integrating Cortex functions into data pipelines.

NEW QUESTION # 272
A financial institution wants to develop a Snowflake-based pipeline to process call transcripts from their customer support. The pipeline needs to perform two main tasks: first, ''summarize very lengthy technical support calls'' (up to 20,000 tokens per transcript) into concise actionable insights, and second, ''classify the sentiment'' of these calls as 'positive', 'neutral', or 'negative'. Given these requirements for integration into SQL data pipelines, which combination of Snowflake Cortex functions and prompt engineering considerations would be most appropriate?

  • A. Option C
  • B. Option E
  • C. Option B
  • D. Option A
  • E. Option D
Answer: C
Explanation:
For summarizing very lengthy technical support calls (up to 20,000 tokens), a model with a sufficiently large context window is essential. (the updated version of offers flexibility for detailed summarization with prompt engineering. A model like 'mistral-large? has a context window of 128,000 tokens, making it suitable for such long inputs. Encapsulating complex prompt logic within a SQL User Defined Function (UDF) is a recommended practice for better management and reusability in data pipelines. For classifying sentiment into predefined categories ('positive', 'neutral', 'negative'), (the updated version of is purpose-built and directly returns the classification label. A. is a generic summarization function, but 'AI_COMPLETE with a large model provides more control for 'actionable insights'. returns a numerical score, requiring additional logic for categorical output. C. 'SNOWFLAKE.CORTEX.EXTRACT ANSWER()' is designed to extract specific answers to questions, not to summarize text. Using it multiple times for summarization would be inefficient and less effective. While can perform classification, is the specialized function for this task. D. 'gemma-7b' has a context window of 8,000 tokens, which is insufficient for processing calls up to 20,000 tokens, potentially leading to truncation or incomplete results. E. and SUMMARIZE AGG()' are designed to aggregate insights or summaries 'across multiple rows' or groups of text, not to summarize a single, lengthy document. returns a boolean result, making it less suitable for multi-category classification directly.

NEW QUESTION # 273
A data engineering team is preparing a large corpus of unstructured text documents for a Retrieval Augmented Generation (RAG) application in Snowflake, leveraging Cortex Search and LLM functions. They plan to use SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER as part of their data ingestion pipeline. What is the primary benefit of employing this helper function in the context of their RAG workflow?
  • A. It compresses the text data to reduce storage costs in Snowflake stages before processing by embedding models.
  • B. It generates vector embeddings for each document chunk, eliminating the need for separate embedding models.
  • C. It divides lengthy documents into smaller, manageable text chunks, which improves the precision of information retrieval and the relevance of downstream LLM responses.
  • D. It performs sentiment analysis on each chunk, allowing the RAG system to filter out negative or irrelevant content before retrieval.
  • E. It automatically translates documents into a target language, ensuring multilingual compatibility for the LLM.
Answer: C
Explanation:
Option C is correct because

is a helper function designed to assist in splitting text into smaller chunks. This is crucial for RAG applications because, for best search results with Cortex Search, it's recommended to split text into smaller chunks, which typically leads to higher retrieval precision and improved downstream LLM response quality. Option A is incorrect; translation is handled by functions like

Option B is incorrect; generating vector embeddings is the role of embedding functions such as


NEW QUESTION # 274
A data scientist is working on a new feature that involves querying a Cortex Search Service and integrating the results into various downstream processes. They are concerned about potential data type compatibility issues and limitations within the Snowflake environment. Which of the following statements accurately describe how to query a Cortex Search Service or the limitations of the 'VECTOR data type and Cortex Search itself? (Select all that apply)
  • A. Cortex Search Services can be configured as a source for Snowflake dynamic tables, enabling continuous and automated synchronization of the search index with base data.
  • B. The 'VECTOR data type, which stores the output of embedding models like those used by Cortex Search, is explicitly not supported in 'VARIANT columns.
  • C. Cortex Search Services can only be queried using the 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' SQL function and do not offer a programmatic interface for applications.
  • D. The "VECTOR data type, used to store embeddings generated for Cortex Search, is fully supported as a primary key in Snowflake's hybrid tables to accelerate similarity searches.
  • E. To retrieve the support ticket most relevant to a query about 'internet issues', filtered to return results only in the 'North America' region, one can use the 'filter' parameter in the 'SEARCH PREVIEW function's JSON argument like so:

Answer: B,E
Explanation:
Option A is incorrect. Cortex Search Services can be queried using both the 'SNOWFLAKE.CORTEX.SEARCH PREVIEW' SQL function and programmatically via the Python API. Option B is incorrect. While the 'VECTOR data type is allowed in hybrid tables, it is not supported as a primary key or secondary index key. Option C is correct. The 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' function accepts a JSON object as an argument, which can include a 'filter' parameter to refine search results based on specified conditions, such as filtering by region. Option D is correct. The 'VECTOR data type is explicitly not supported in 'VARIANT columns, meaning embeddings cannot be stored directly within semi-structured data in a single 'VARIANT column. Option E is incorrect. Snowflake Cortex functions, including Cortex Search, do not support dynamic tables.

NEW QUESTION # 275
A security audit is being conducted for a financial institution using Snowflake Cortex. Which of the following statements accurately describe Snowflake's data safety and security guarantees concerning whether customer data, metadata, or prompts leave Snowflake's governance boundary to a third-party when using Cortex features, under the default Snowflake configurations for Cortex functions unless otherwise specified?
  • A. When CORTEX_ENABLED_CROSS_REGION is active for Cortex LLM functions, user inputs and outputs are always cached in the intermediate region to reduce latency, thereby leaving the primary region's immediate governance.
  • B. When using SNOWFLAKE .CORTEX. COMPLETE with Snowflake-hosted LLMs like all prompts and generated responses remain within Snowflake's mistral-large2, governance boundary by default.
  • C. Models brought into Snowflake via Snowpark Container Services (BYOM) are treated as Snowflake's proprietary models, meaning Snowflake assumes responsibility for their data handling policies.
  • D. Customer Data and inputs to Snowflake AI Features are never used by Snowflake to train or fine-tune models made available to other customers.
  • E. For Cortex Analyst, if the legacy ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAI account parameter is set to TRUE, customer metadata and prompts are transmitted to Azure OpenAI, but the underlying customer data is not.
Answer: B,D,E
Explanation:
Option A is correct because Snowflake explicitly states that Usage and Customer Data (including inputs and outputs) are NOT used to train, re-train, or fine-tune Models made available to others, and fine-tuned Models are available exclusively for the customer's use. Option B is correct as all models powering Snowflake Cortex AI functions are fully hosted in Snowflake, ensuring performance, scalability, and governance while keeping customer data secure and in place within Snowflake's governance boundary. Option C is correct as this describes a specific, legacy exception for Cortex Analyst: if the 'ENABLE CORTEX ANALYST MODEL_AZURE OPENAI' parameter is 'TRUE', then *only metadata and prompts* are transmitted outside of Snowflake's governance boundary to Microsoft Azure (a third party), while Customer Data itself is not shared. Option D is incorrect because models brought into the Service account (BYOM), for example via Snowpark Container Services, are treated as Customer Data, not Snowflake's proprietary models, and are subject to the customer's own rights and obligations as per their Customer Agreement. Option E is incorrect because when 'CORTEX ENABLED_CROSS REGION' is enabled, user inputs, service generated prompts, and outputs are explicitly *not stored or cached* during cross-region inference.

NEW QUESTION # 276
......
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