Snowflake Snowflake Certification GES-C01

덤프는 세가지 버전으로 제공가능

GES-C01시험덤프는 3개 버전으로 되어있는데PDF버전은 출력하여 어디에서든 공부가능하고 소프트버전과 온라인버전은 PDF버전의 내용과 동일한데 PDF버전 공부를 마친후 실력테스트 가능한 프로그램입니다. GES-C01덤프의 소프트웨어버전은 PC에 JAVA시스템을 설치하면 작동가능하고 Snowflake Snowflake Certification온라인버전은 PC뿐만아니라 휴대폰에서도 사용가능하기에 소프트웨어버전을 업그레이드한 버전이라고 보시면 됩니다.

인증시험을 쉽게 패스하는 방법

GES-C01시험은 IT업종에 종사하시는 분들께 널리 알려진 유명한 자격증을 취득할수 있는 시험과목입니다. GES-C01시험은 영어로 출제되는 만큼 시험난이도가 많이 높습니다.하지만 GES-C01인증덤프만 있다면 아무리 어려운 시험도 쉬워집니다. 오르지 못할 산도 정복할수 있는게 저희 제품의 우점입니다. GES-C01덤프로 GES-C01시험을 패스하여 자격증을 취득하면 정상에 오를수 있습니다.

일년무료 업데이트 서비스 제공

GES-C01덤프는 IT업계에 오랜 시간동안 종사하셨던 베테랑 전문가들이 오랜 시간동안 쌓아온 풍부한 경험과 IT지식으로 만들어낸 최고의 제품입니다. GES-C01덤프자료는 적중률 높은 전면적인 덤프임으로 여러분은 한번에 GES-C01시험을 패스하실수 있습니다. GES-C01덤프를 구매하시면 덤프유효성을 연장해드리도록 1년무료 업데이트 서비스를 제공해드립니다.

IT업계 엘리트한 강사들이 퍼펙트한 GES-C01시험자료 덤프문제집을 제작하여 디테일한 시험문제와 답으로 여러분이 아주 간단히 GES-C01인증시험을 패스할 수 있도록 최선을 다하고 있습니다.

Snowflake GES-C01 시험 요강 주제:

최신 Snowflake Certification GES-C01 무료샘플문제

1. A Gen AI engineer is tasked with selecting the most suitable Large Language Model (LLM) from Snowflake Cortex AI for a new customer service chatbot. They need to rapidly prototype and compare different LLMs with varying parameters on a sample dataset before committing to a production deployment. Which of the following statements accurately describe how the Cortex Playground (Public Preview) can assist in this scenario?

A) It provides a mechanism to deploy the chosen LLM directly into Snowpark Container Services (SPCS) compute pools from within the playground for immediate production use.
B) It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.
C) It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
D) It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
E) It enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.

2. A company is planning to process a large volume of legal documents to generate summaries using SNOWFLAKE. CORTEX. SUMMARIZE. Given the scale, they are acutely focused on managing costs and optimizing performance. Which of the following statements are true regarding the cost and performance characteristics of using SNOWFLAKE. CORTEX. SUMMARIZE? (Select all that apply)

A) For SUWARIZE, Snowflake adds an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.
B) The SUWARIZE function is billed primarily based on the number of output tokens generated in the response, not input tokens.
C) The context window for the SWIMARIZE function is 4,096 tokens, ensuring efficiency for short documents only.
D) Snowflake recommends using a larger warehouse (e.g., L or XL) for SUMMARIZE function calls to significantly improve processing performance for high-volume tasks.
E) The fixed billing rate for the SUMMARIZE function is 0.10 Credits per one million Tokens processed.

3. 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
B) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
C) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
D) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
E) Using the

4. A Snowflake team observes consistently high token costs from 'SNOWFLAKE.ACCOUNT USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY' for a summarization task using the 'mistral- large? model. The task involves summarizing legal documents, which often exceed the context window of common LLMs. To optimize these token-based costs, which strategy should the team prioritize?

A) Enable Cortex Guard for the COMPLETE function calls, as its filtering capabilities automatically reduce the number of tokens processed for unsafe content.
B) Set the temperature parameter to 0 in the COMPLETE function options to ensure more deterministic and thus more cost-efficient summarization outputs.
C) Switch from using the COMPLETE function to TRY_COMPLETE to automatically avoid billing for queries that fail due to context window limits, thereby reducing costs.
D) Increase the virtual warehouse size (e.g., from X-SMALL to MEDIUM) used for running the summarization queries to boost performance and reduce overall cost per query.
E) Implement a text splitting mechanism, potentially using SPLIT_TEXT_RECURSIVE_CHARACTER, to break down lengthy documents into smaller chunks before passing them to the summarization function, then aggregate the summaries.

5. A developer is building a real-time chat application and wants to integrate a Large Language Model (LLM) hosted in Snowflake Cortex using its REST API. They need to send user prompts and receive streaming responses, ensuring secure authentication. Which of the following statements about using the Cortex REST API for the COMPLETE function are correct?

A) Option D
B) Option B
C) Option A
D) Option E
E) Option C

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