GES-C01試験無料問題集「Snowflake SnowPro® Specialty: Gen AI Certification 認定」
A Snowflake administrator is tasked with ensuring that a specific data science team can only use approved LLMs (mistral-7b, llama3.1-8b) for generative AI tasks within a particular schema, and also needs to enable the use of an LLM in a non-native region due to specific project requirements. Which combination of configurations would meet these requirements?
正解：D,E
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A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses 'invoice_model!PREDlCT' to extract data from daily financial reports. They observe that their assigned 'LARGE virtual warehouse is running continuously, even during periods of low document ingestion, contributing significantly to their bill. They want to investigate how to reduce costs effectively for their existing Document AI setup.
正解：E
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A developer is building an interactive chat application in Snowflake leveraging the COMPLETE (SNOWFLAKE. CORTEX) LLM function to power multi-turn conversations. To ensure the LLM maintains conversational context and generates coherent responses based on prior interactions, which of the following methods correctly implements the passing of conversation history to the COMPLETE function?
正解：E
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A global marketing team uses Snowflake to manage customer feedback in various languages. They need to translate customer reviews from German ("de") into English ("en") for analysis. The reviews are stored in a table named 'CUSTOMER REVIEWS' in a column called 'REVIEW TEXT'. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.TRANSLATE function and what is the expected return type for the translated text?
正解：A
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An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?
正解：A,B,D,E
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A Snowflake developer, named ANALYST USER, is tasked with creating a Streamlit in Snowflake (SiS) application that will utilize both SNOWFLAKE. CORTEX. COMPLETE for generating responses and SNOWFLAKE. CORTEX.CLASSIFY_TEXT for categorizing user input. To ensure the role used by ANALYST USER has the necessary permissions for executing these Cortex LLM functions and operating within a specified database and schema, which of the following database roles or privileges must be granted? (Select all that apply.)
正解：D,E
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A development team is implementing a document retrieval system in Snowflake. They plan to store document embeddings and use VECTOR_L2_DISTANCE to find the most relevant documents for a given query embedding. Considering Snowflake's capabilities, which of the following statements are true regarding the use of vector types and VECTOR_L2_DISTANCE
? (Select all that apply)
? (Select all that apply)
正解：C,D,E
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A data engineer is configuring a Document AI pipeline to process scanned PDF invoices stored in an internal stage named 'invoice_docs_stage'. After uploading the PDF files, they execute an extracting query using '!PREDICT. The query consistently returns the error:
Which of the following is the most likely cause of this error?
Which of the following is the most likely cause of this error?
正解：C
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A data science team is developing an internal LLM to classify legal documents. They previously used a general-purpose LLM, but found its performance for their specific legal domain to be inconsistent, leading to high error rates and increased manual review. They decide to fine-tune a model using Snowflake Cortex Fine-tuning to improve accuracy and reduce latency for real-time document classification. Which base model, among those available for fine-tuning via SNOWFLAKE .CORTEX.FINETUNE
, is explicitly noted for its low latency and high throughput processing, making it a strong candidate for this use case, especially for multi-page text classification?
, is explicitly noted for its low latency and high throughput processing, making it a strong candidate for this use case, especially for multi-page text classification?
正解：B
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A team is planning the implementation of a new Document AI solution and needs to be aware of the specific guidelines and limitations concerning naming conventions and task management within Snowflake. A primary concern is to avoid common pitfalls that could lead to errors or unsupported configurations.
正解：C,E
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A project team is preparing to deploy a Document AI solution to process scanned customer feedback forms. They have created a dedicated role, 'customer feedback _ processor', and successfully granted it the SNOWFLAKE. DOCUMENT_INTELLIGENCE_CREATOR database role. The environment consists of 'feedback database, 'forms schema' schema, and 'ai workload warehouse. However, when the attempts to prepare a Document AI model build in Snowsight, they encounter a 'permission denied' error. Which of the following missing 'USAGE' grants could be the direct cause of this error?
正解：A,B,D
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A Gen AI developer has a Document AI pipeline that uses a query with 'GET PRESIGNED URL' to process multi-page PDF documents. Despite the internal stage being correctly set up with 'SNOWFLAKE SSE' encryption and the model build being published, they observe inconsistent results. Some documents result in a Received HTTP 403 response for presigned URL. URL may be expired.
error, while other documents (containing complex diagrams and dense text in an unsupported language like Korean) are processed, but the extracted information is often incomplete or inaccurate.
Which two factors are most likely contributing to these observed issues?
error, while other documents (containing complex diagrams and dense text in an unsupported language like Korean) are processed, but the extracted information is often incomplete or inaccurate.
Which two factors are most likely contributing to these observed issues?
正解：C,D
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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?
正解：E
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