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GES-C01一発合格、GES-C01日本語

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Snowflake SnowPro® Specialty: Gen AI Certification Exam 認定 GES-C01 試験問題 (Q248-Q253):

質問 # 248
A data application developer is building a Streamlit chat application that interacts with structured data using Cortex Analyst, a feature of Snowflake Cortex that is often integrated into Cortex Agents. The developer wants to ensure efficient multi-turn conversations and leverage verified queries. Which of the following statements represent best practices and functional aspects for this scenario within Snowflake's Gen AI ecosystem?

A. Option B
B. Option A
C. Option D
D. Option E
E. Option C

正解：A、C

解説：
The 'verified_queries' section of the semantic model must use the names of the logical tables and columns defined in the semantic model, not those in the underlying dataset. This makes option B correct. For suggested questions in Cortex Analyst, configuring onboarding questions is done by setting the 'use_as_onboarding_question' flag to 'true' in the verified query definition within the semantic model, which will return all such flagged questions regardless of similarity to user input. This makes option D correct. Option A is incorrect because Snowflake Cortex Analyst 'adds' an LLM summarization agent internally to support multi-turn conversations, it is not something the application developer has to implement separately. Option C is incorrect; while larger models generally have better performance, Cortex Analyst aims to select the smallest model that satisfies the need, balancing latency and performance. Option E describes Document AI's ' !PREDICT' method, which extracts information from documents, but it is not directly how a Cortex Agent (which orchestrates and uses tools) would 'process documents' in a general sense within a Streamlit app; rather, Cortex Agents might 'use' Document AI as a tool or Cortex Parse Document or other methods, not necessarily ' !PREDICT' directly in the chat logic.

質問 # 249
A global enterprise has Snowflake accounts in various regions, including a US East (Ohio) account where a critical application is deployed. They need to use AI_COMPLETE with the claude-3-5-sonnet model for real-time customer support, but this model is not natively available in US East (Ohio) for direct AI_COMPLETE usage. The Snowflake administrator considers enabling cross-region inference. Which statements accurately reflect the considerations and characteristics of cross-region inference in Snowflake Cortex?

A. Option B
B. Option A
C. Option D
D. Option C
E. Option E

正解：A、D

解説：
Option B is correct because setting the parameter to 'ANY_REGION' enables inference requests to be CORTEX_ENABLED_CROS_ REGION processed in a different region from the default, thereby allowing access to models not natively supported in the local region. For example, claude- 3 - 5- sonnet is available in AWS US East 1 (N. Virginia), which could be accessed from US East (Ohio) via cross-region inference. Option C is 3-5 -sonnet correct as cross-region inference is explicitly not supported in U.S. SnowGov regions for either inbound or outbound inference requests. Option A is incorrect because user inputs, service generated prompts, and outputs are not stored or cached during cross-region inference. Option D is incorrect; latency depends on the cloud provider infrastructure and network status, and testing is recommended. Option E is incorrect because CORTEX_ENABLED_CROSS_REGION is an account-level parameter, not a session parameter.

質問 # 250
A development team is building a RAG application in Snowflake Cortex that needs to extract high-fidelity text and layout from a collection of technical documentation PDFs stored in an internal stage to power semantic search and LLM responses. They want to ensure proper context retrieval for complex user queries. Given this scenario, which of the following actions or statements are crucial for effectively leveraging AI_PARSE_DOCUMENT to optimize the RAG pipeline?

A. Option B
B. Option A
C. Option D
D. Option C
E. Option E

正解：A、D

解説：

質問 # 251
A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

A. When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
B. Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.
C. Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.
D. Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.
E. The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.

正解：A、D、E

解説：
Option B is correct: when Cortex Guard is enabled and a response is blocked, the model's output is replaced by a message (defaulting to 'Response filtered by Cortex Guard'), and only 'guard_tokens' are counted as input tokens for Cortex Guard's processing, in addition to the primary LLM's prompt and completion tokens. Option C is correct as the argument allows customization of the filtered response message for both 'COMPLETE' and the REST API. Option E is correct because Cortex Guard is built with Meta's Llama Guard 3 and its usage is billed separately as 'guard_tokens' in addition to the 'COMPLETE function cost. Option A is incorrect because while guard_tokens' are billed, it's specifically for the guardrail processing, and the 'unsafe' completion tokens are not returned or billed as such, rather replaced by a filtered message. Option D is incorrect because Cortex Guard requires explicit enablement by setting 'guardrails' to 'TRUE'.

質問 # 252
A machine learning team has fine-tuned a llama3.1-70b model for a specialised task using Snowflake Cortex Fine-tuning, named prod_llama_responder. They now need to deploy this model for inference via the Cortex REST API across different Snowflake regions and manage its lifecycle effectively. Which of the following statements regarding the fine-tuned model's deployment, access, and management are accurate?

A. Option B
B. Option A
C. Option D
D. Option E
E. Option C

正解：A、C、D

解説：

質問 # 253
......

今日、社会での競争はより激しく、専門知識がなければ競争で有利な地位を占めることができず、除かれることもあります。テストGES-C01認定に合格すると、一部の分野で有能になり、労働市場で競争上の優位性を獲得できます。 GES-C01学習教材を購入すると、GES-C01テストにスムーズに合格します。当社Snowflakeの製品は多くの利点を高め、テストの準備をするのに最適です。 GES-C01学習準備は、一流の専門家チームによってコンパイルされ、実際の試験と密接にリンクしています。

GES-C01一発合格: https://www.mogiexam.com/GES-C01-exam.html

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試験の準備方法-最新のGES-C01テスト難易度試験-信頼的なGES-C01一発合格

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